{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据量比较大，然后需要进行一些处理。主要通过订单的数据来进行一些数据分析。\n",
    "# 1读取数据\n",
    "# 2数据探索\n",
    "# 3数据预处理与可视化分析\n",
    "## 3.1合并字段与处理缺失值\n",
    "## 3.2字段之间相关性可视化\n",
    "## 3.3按照不同的Market,Order R，Category Name对销售额度进行分析Sales per customer并且可视化\n",
    "## 3.4按照不同的时间维度对销售额进行分析\n",
    "## 3.5用户RFM分层管理\n",
    "## 3.6不同地区的支付情况分析与可视化\n",
    "## 3.7对负收益的订单进行数据分析\n",
    "## 3.8欺诈订单分析（支付方式、地区、哪些人欺诈订单较多）\n",
    "## 4文件转存为pkl格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Order Zipcode</th>\n",
       "      <th>Product Card Id</th>\n",
       "      <th>Product Category Id</th>\n",
       "      <th>Product Description</th>\n",
       "      <th>Product Image</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>314.640015</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>134.210007</td>\n",
       "      <td>298.250000</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1360</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Smart+watch</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 11:24</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 3:40</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>141.110001</td>\n",
       "      <td>391.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 21:00</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>186.229996</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 20:18</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168.949997</td>\n",
       "      <td>383.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004</td>\n",
       "      <td>45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://images.acmesports.sports/Field+%26+Stre...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 53 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0          DEBIT                         3                              4   \n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "3          DEBIT                         3                              4   \n",
       "4        PAYMENT                         2                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180514      CASH                         4                              4   \n",
       "180515     DEBIT                         3                              2   \n",
       "180516  TRANSFER                         5                              4   \n",
       "180517   PAYMENT                         3                              4   \n",
       "180518   PAYMENT                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "4              134.210007          298.250000  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180514          40.000000          399.980011  Shipping on time   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "180516         141.110001          391.980011     Late delivery   \n",
       "180517         186.229996          387.980011  Advance shipping   \n",
       "180518         168.949997          383.980011  Shipping on time   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                        0           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180514                   0           45         Fishing      Brooklyn  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "180516                   1           45         Fishing       Bristol  ...   \n",
       "180517                   0           45         Fishing        Caguas  ...   \n",
       "180518                   0           45         Fishing        Caguas  ...   \n",
       "\n",
       "       Order Zipcode Product Card Id Product Category Id  Product Description  \\\n",
       "0                NaN            1360                  73                  NaN   \n",
       "1                NaN            1360                  73                  NaN   \n",
       "2                NaN            1360                  73                  NaN   \n",
       "3                NaN            1360                  73                  NaN   \n",
       "4                NaN            1360                  73                  NaN   \n",
       "...              ...             ...                 ...                  ...   \n",
       "180514           NaN            1004                  45                  NaN   \n",
       "180515           NaN            1004                  45                  NaN   \n",
       "180516           NaN            1004                  45                  NaN   \n",
       "180517           NaN            1004                  45                  NaN   \n",
       "180518           NaN            1004                  45                  NaN   \n",
       "\n",
       "                                            Product Image  \\\n",
       "0            http://images.acmesports.sports/Smart+watch    \n",
       "1            http://images.acmesports.sports/Smart+watch    \n",
       "2            http://images.acmesports.sports/Smart+watch    \n",
       "3            http://images.acmesports.sports/Smart+watch    \n",
       "4            http://images.acmesports.sports/Smart+watch    \n",
       "...                                                   ...   \n",
       "180514  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180515  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180516  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180517  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "180518  http://images.acmesports.sports/Field+%26+Stre...   \n",
       "\n",
       "                                     Product Name Product Price  \\\n",
       "0                                    Smart watch     327.750000   \n",
       "1                                    Smart watch     327.750000   \n",
       "2                                    Smart watch     327.750000   \n",
       "3                                    Smart watch     327.750000   \n",
       "4                                    Smart watch     327.750000   \n",
       "...                                           ...           ...   \n",
       "180514  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180516  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180517  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180518  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status shipping date (DateOrders)   Shipping Mode  \n",
       "0                   0             2/3/2018 22:56  Standard Class  \n",
       "1                   0            1/18/2018 12:27  Standard Class  \n",
       "2                   0            1/17/2018 12:06  Standard Class  \n",
       "3                   0            1/16/2018 11:45  Standard Class  \n",
       "4                   0            1/15/2018 11:24  Standard Class  \n",
       "...               ...                        ...             ...  \n",
       "180514              0             1/20/2016 3:40  Standard Class  \n",
       "180515              0             1/19/2016 1:34    Second Class  \n",
       "180516              0            1/20/2016 21:00  Standard Class  \n",
       "180517              0            1/18/2016 20:18  Standard Class  \n",
       "180518              0            1/19/2016 18:54  Standard Class  \n",
       "\n",
       "[180519 rows x 53 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset=pd.read_csv('SupplyChain.csv',encoding='unicode_escape')\n",
    "dataset#数据量比较大  180519行  53列"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type                                  0\n",
       "Days for shipping (real)              0\n",
       "Days for shipment (scheduled)         0\n",
       "Benefit per order                     0\n",
       "Sales per customer                    0\n",
       "Delivery Status                       0\n",
       "Late_delivery_risk                    0\n",
       "Category Id                           0\n",
       "Category Name                         0\n",
       "Customer City                         0\n",
       "Customer Country                      0\n",
       "Customer Email                        0\n",
       "Customer Fname                        0\n",
       "Customer Id                           0\n",
       "Customer Lname                        8\n",
       "Customer Password                     0\n",
       "Customer Segment                      0\n",
       "Customer State                        0\n",
       "Customer Street                       0\n",
       "Customer Zipcode                      3\n",
       "Department Id                         0\n",
       "Department Name                       0\n",
       "Latitude                              0\n",
       "Longitude                             0\n",
       "Market                                0\n",
       "Order City                            0\n",
       "Order Country                         0\n",
       "Order Customer Id                     0\n",
       "order date (DateOrders)               0\n",
       "Order Id                              0\n",
       "Order Item Cardprod Id                0\n",
       "Order Item Discount                   0\n",
       "Order Item Discount Rate              0\n",
       "Order Item Id                         0\n",
       "Order Item Product Price              0\n",
       "Order Item Profit Ratio               0\n",
       "Order Item Quantity                   0\n",
       "Sales                                 0\n",
       "Order Item Total                      0\n",
       "Order Profit Per Order                0\n",
       "Order Region                          0\n",
       "Order State                           0\n",
       "Order Status                          0\n",
       "Order Zipcode                    155679\n",
       "Product Card Id                       0\n",
       "Product Category Id                   0\n",
       "Product Description              180519\n",
       "Product Image                         0\n",
       "Product Name                          0\n",
       "Product Price                         0\n",
       "Product Status                        0\n",
       "shipping date (DateOrders)            0\n",
       "Shipping Mode                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看一下是否有缺失值\n",
    "dataset.isna().sum()#可以看到是有缺失值的 #Customer Lname 有8个缺失值  #Customer Zipcode有3个缺失值 \n",
    "#Order Zipcode 有155679个缺失值      Product Description 有180519个缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Customer Lname              8\n",
       "Customer Zipcode            3\n",
       "Order Zipcode          155679\n",
       "Product Description    180519\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#可以用这么一种方法把缺失值大于0的字段找出来\n",
    "temp=dataset.isna().sum()\n",
    "temp[temp>0]#这样就把有缺失值的 给单独这两个出来了 Customer Zipcode是用户邮编 Order Zipcode是订单的邮编  Product Description是产品的描述"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Type', 'Days for shipping (real)', 'Days for shipment (scheduled)', 'Benefit per order', 'Sales per customer', 'Delivery Status', 'Late_delivery_risk', 'Category Id', 'Category Name', 'Customer City', 'Customer Country', 'Customer Email', 'Customer Fname', 'Customer Id', 'Customer Lname', 'Customer Password', 'Customer Segment', 'Customer State', 'Customer Street', 'Customer Zipcode', 'Department Id', 'Department Name', 'Latitude', 'Longitude', 'Market', 'Order City', 'Order Country', 'Order Customer Id', 'order date (DateOrders)', 'Order Id', 'Order Item Cardprod Id', 'Order Item Discount', 'Order Item Discount Rate', 'Order Item Id', 'Order Item Product Price', 'Order Item Profit Ratio', 'Order Item Quantity', 'Sales', 'Order Item Total', 'Order Profit Per Order', 'Order Region', 'Order State', 'Order Status', 'Order Zipcode', 'Product Card Id', 'Product Category Id', 'Product Description', 'Product Image', 'Product Name', 'Product Price', 'Product Status', 'shipping date (DateOrders)', 'Shipping Mode']\n"
     ]
    }
   ],
   "source": [
    "print(dataset.columns.tolist())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3数据预处理与可视化分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1合并字段与处理缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Lname</th>\n",
       "      <th>Customer Fname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Holloway</td>\n",
       "      <td>Cally</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Luna</td>\n",
       "      <td>Irene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Maldonado</td>\n",
       "      <td>Gillian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tate</td>\n",
       "      <td>Tana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hendricks</td>\n",
       "      <td>Orli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>Peterson</td>\n",
       "      <td>Maria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>Clark</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>Smith</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>Smith</td>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>Ortega</td>\n",
       "      <td>Andrea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer Lname Customer Fname\n",
       "0            Holloway          Cally\n",
       "1                Luna          Irene\n",
       "2           Maldonado        Gillian\n",
       "3                Tate           Tana\n",
       "4           Hendricks           Orli\n",
       "...               ...            ...\n",
       "180514       Peterson          Maria\n",
       "180515          Clark         Ronald\n",
       "180516          Smith           John\n",
       "180517          Smith           Mary\n",
       "180518         Ortega         Andrea\n",
       "\n",
       "[180519 rows x 2 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#单独把name 有姓也有名 给提出来看看\n",
    "dataset[['Customer Lname','Customer Fname']]#把二者合并一下吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>Customer Lname</th>\n",
       "      <th>Customer Fname</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HollowayCally</td>\n",
       "      <td>Holloway</td>\n",
       "      <td>Cally</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LunaIrene</td>\n",
       "      <td>Luna</td>\n",
       "      <td>Irene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MaldonadoGillian</td>\n",
       "      <td>Maldonado</td>\n",
       "      <td>Gillian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TateTana</td>\n",
       "      <td>Tate</td>\n",
       "      <td>Tana</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HendricksOrli</td>\n",
       "      <td>Hendricks</td>\n",
       "      <td>Orli</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>PetersonMaria</td>\n",
       "      <td>Peterson</td>\n",
       "      <td>Maria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>ClarkRonald</td>\n",
       "      <td>Clark</td>\n",
       "      <td>Ronald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>SmithJohn</td>\n",
       "      <td>Smith</td>\n",
       "      <td>John</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>SmithMary</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Mary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>OrtegaAndrea</td>\n",
       "      <td>Ortega</td>\n",
       "      <td>Andrea</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer Full Name Customer Lname Customer Fname\n",
       "0           HollowayCally       Holloway          Cally\n",
       "1               LunaIrene           Luna          Irene\n",
       "2        MaldonadoGillian      Maldonado        Gillian\n",
       "3                TateTana           Tate           Tana\n",
       "4           HendricksOrli      Hendricks           Orli\n",
       "...                   ...            ...            ...\n",
       "180514      PetersonMaria       Peterson          Maria\n",
       "180515        ClarkRonald          Clark         Ronald\n",
       "180516          SmithJohn          Smith           John\n",
       "180517          SmithMary          Smith           Mary\n",
       "180518       OrtegaAndrea         Ortega         Andrea\n",
       "\n",
       "[180519 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Customer Full Name']=dataset['Customer Lname']+dataset['Customer Fname']\n",
    "dataset[['Customer Full Name','Customer Lname','Customer Fname']]#看看合并的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "725.0      66770\n",
       "921.0        337\n",
       "23455.0      334\n",
       "957.0        297\n",
       "79109.0      292\n",
       "           ...  \n",
       "60636.0       18\n",
       "89015.0       16\n",
       "32210.0       15\n",
       "7728.0        13\n",
       "11225.0        9\n",
       "Name: Customer Zipcode, Length: 995, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#同时再探索一下Zipcode\n",
    "dataset['Customer Zipcode'].value_counts()#有三个缺失值  可以用0填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['Customer Zipcode']=dataset['Customer Zipcode'].fillna(0)\n",
    "dataset['Customer Zipcode'].isna().sum()#填充完之后就没有0了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2字段之间相关性可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看特征字段之间的相关性\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "#特征字段之间的相关性\n",
    "data=dataset\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(data.corr(),annot=True)#可以看到还是有很多特征是高度相关的  都成1了 这个时候需要去重"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LATAM           51594\n",
       "Europe          50252\n",
       "Pacific Asia    41260\n",
       "USCA            25799\n",
       "Africa          11614\n",
       "Name: Market, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Market'].value_counts()#主要是有这几个超市 market  然后需要按照超市来看看销售额"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3按照不同的Market,Order R，Category Name对销售额度进行分析Sales per customer并且可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Market\n",
       "Europe          9.769198e+06\n",
       "LATAM           9.235762e+06\n",
       "Pacific Asia    7.434263e+06\n",
       "USCA            4.553500e+06\n",
       "Africa          2.061679e+06\n",
       "Name: Sales per customer, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照不同的Market,Order R，Category Name对销售额度进行分析Sales per customer\n",
    "#基于Market\n",
    "market=data.groupby('Market')#按照‘Market’进行聚合一下\n",
    "market['Sales per customer'].sum().sort_values(ascending=False)#这里就得出了 5个超市 的销售额度 并且排序显示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'各个超市的销售额度情况'}, xlabel='Market'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t5PhRVfX6qrqoql64sSUCAMDG220Qrqr7Jzmou++U5NiqOn6NZg9P8vLuPiXJdavqxA2uEwAANtR6eoRPTXLOvH1+kpPXaPOpJLesqusl+Y4kH1ndoKrOqKqtVbV127Zte1YtAABskPUE4SOSXD5vX5HkmDXaXJzk+CSPTfKBJJ9e3aC7z+ruE7v7xC1btuxhuQAAsDHWE4SvTHL4vH3kTu7zm0l+prufmikIP2pjygMAgL1jPUH4kuwYDnFCksvWaHOdJLepqoOS3CFJb0h1AACwl6wnCJ+b5OFV9ewkD0ry/qp62qo2T09yVpLPJrl+klduZJEAALDRDt5dg+6+oqpOTXJakmd198eTXLqqzTuS3HpvFAgAAHvDboNwknT3p7Nj5QgAANjvubIcAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQDl66AID9wXFnnrd0CZvCZc+499IlAGwYPcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSOsKwlV1dlW9paqetJt2L6iqH96Y0gAAYO/ZbRCuqvsnOai775Tk2Ko6fiftTklyw+7+PxtcIwAAbLj19AifmuScefv8JCevblBVhyR5cZLLquo+G1YdAADsJesJwkckuXzeviLJMWu0+Ykkf5/kWUl+oKp+YXWDqjqjqrZW1dZt27btab0AALAh1hOEr0xy+Lx95E7u871Jzurujyd5eZK7rG7Q3Wd194ndfeKWLVv2tF4AANgQ6wnCl2THcIgTkly2RpsPJbn5vH1ikg9/05UBAMBedPA62pyb5KKqOjbJvZI8pKqe1t0rV5A4O8kfVtVDkhyS5AEbXikAAGyg3Qbh7r6iqk5NclqSZ83DHy5d1eZzSR64NwoEAIC9YT09wunuT2fHyhEAALDfc2U5AACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQDl66AADYHx135nlLl7BpXPaMey9dAuwRPcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGtK4gXFVnV9VbqupJu2l3TFX93caUBgAAe89ug3BV3T/JQd19pyTHVtXxu2j+20kO36jiAABgb1lPj/CpSc6Zt89PcvJajarqrkk+n+TjG1IZAADsResJwkckuXzeviLJMasbVNWhSZ6c5MydnaSqzqiqrVW1ddu2bXtSKwAAbJj1BOErs2O4w5E7uc+ZSZ7f3Z/Z2Um6+6zuPrG7T9yyZcs1LhQAADbSeoLwJdkxHOKEJJet0ebuSX6uqi5McruqesmGVAcAAHvJwetoc26Si6rq2CT3SvKQqnpad//nChLdfeft21V1YXf/1IZXCgAAG2i3Qbi7r6iqU5OcluRZ3f3xJJfuov2pG1UcAADsLevpEU53fzo7Vo4AAID9nivLAQAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAY0rqCcFWdXVVvqaon7eT4t1bVX1TVG6rqz6rq0I0tEwAANtbBu2tQVfdPclB336mqXlBVx3f3B1c1+7Ekz+7uN1TVHyQ5Pcmf74V6AQA2rePOPG/pEjaNy55x76VL2K3dBuEkpyY5Z94+P8nJSb4uCHf3C1bc3JLkExtRHAAA7C3rGRpxRJLL5+0rkhyzs4ZVdcckR3X329Y4dkZVba2qrdu2bdujYgEAYKOsJwhfmeTwefvInd2nqq6f5HlJfnKt4919Vnef2N0nbtmyZU9qBQCADbOeIHxJpuEQSXJCkstWN5gnx52T5And/eENqw4AAPaS9QThc5M8vKqeneRBSd5fVU9b1ebRSb4/yROr6sKqevDGlgkAABtrt5PluvuKqjo1yWlJntXdH09y6ao2f5DkD/ZGgQAAsDesZ9WIdPens2PlCAAA2O+5shwAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhiQIAwAwJEEYAIAhCcIAAAxJEAYAYEiCMAAAQxKEAQAYkiAMAMCQBGEAAIYkCAMAMCRBGACAIQnCAAAMSRAGAGBIgjAAAEMShAEAGJIgDADAkARhAACGJAgDADAkQRgAgCEJwgAADGldQbiqzq6qt1TVk76ZNgAAsFnsNghX1f2THNTdd0pybFUdvydtAABgM1lPj/CpSc6Zt89PcvIetgEAgE3j4HW0OSLJ5fP2FUlusSdtquqMJGfMN6+sqn+8ZqUesI5O8smli6hnLl0Bq3hfsJbF3xfeE5uS9wVr8b7Y4aY7O7CeIHxlksPn7SOzdi/ybtt091lJzlrH4w2lqrZ294lL18Hm4n3BWrwvWIv3BWvxvlif9QyNuCQ7hjqckOSyPWwDAACbxnp6hM9NclFVHZvkXkkeUlVP6+4n7aLNSRtdKAAAbKTd9gh39xWZJsO9LclduvvSVSF4rTaf3fhSD1iGi7AW7wvW4n3BWrwvWIv3xTpUdy9dAwAA7HOuLAcAwJAEYQAAhrSeyXIALKiqtmTHEpU37u63LlkPwIFCEF5QVX1Pkhsn+UiSj3b3lQuXxEKq6g/X2p2ku/sn93U9bB5VdXaSmyU5KskXknRcvZNZVR2S5JQkp3f3f1u6Hpbng/M1IwgvpKqel+TYTH/gfi3JM5P8yKJFsaTrJLltkq8kuTTT2tzvSvLhJYtiU7hpktOTvCLJQzNdxp6BVdUtMr0nTs+0XOl5SS5YtCg2BR+crzljhJdzm+7+0SSf6e7zknzr0gWxnO5+SHffKsldkrw1ycOSvDHJXy1aGJvBVUnuluSgJA/M9AeOQVXVvyR5f5KbJ3l8kvd29yO6+6WLFsZmsf2D84eS/GCSry1bzuYnCC9nW1U9OclRVfWIJB9fuiCWU1Wvqqp/yNSrc8ckr8wUik9ftDA2gwcl+WCS/zfJdyf52WXLYWH3S/LkTFdxfUWS/6eq7lNV11u0KjYLH5yvIesIL6SqDk9yRpJbJvlAkpd09xeWrYqlVNX/XGN3J4kxwsBaquqIJHdNcs8kd+/u71q4JBY2vydulGmY3aOTvKG7L1q2qs3NGOGFdPcXq+pNmSbKfUgIHlt3P2rl7ar69kx/3PQIA1+nqu6bZFt3v7mqviXJ65P8wrJVsUlcneR63b21qj6S5O1LF7TZGRqxkKp6bpKnJrl9kmdW1e8sXBILqqpDq+ruVfXbVfXuJO9NcpMkz1+2MpZSVc+ef15QVefP/y6oKpPlBjZ/e3RSks/Pu65M8l+SrPWtEuM5J8mt5+0bZBo+wy4YGrGQqrq4u0/e2W3GUlWfS3JopuD7P5K8prvvsmxVwGZTVW/q7juvsf+i7j5liZrYPFa/D6rqAn9Lds3QiOX8e1U9ONMSWT+Q5KNVdZPu/sjCdbGMmyS5R6bhEBcnObqqfinJ+d39niULAzaVf5h7hc9L8h9JjkxyWpKPLloVm8XHqurxSd6R6RvnTyxcz6anR3ghKyZHdaYLJyQunsCsqm6TKRTfo7vvsXQ9LKeqrpUp7Hwh04UTtnb355atiqXM74cHZ/r9cEySKzJ9eH5xd39pydpYXlUdlmki/ndlmoh/VndftWxVm5sgvKCqumem5ZDe191/s3Q9LKeq7tPdr126DjafqnpNkrOS/HCS6yc5prvvvmxVLKmqbtDdn5i375zksO5+w8JlwX7JZLmFzBNhHpTki0l+zGS54f3i0gWwaR3d3X+d5Pju/rHsuHQqA6qqpyd56bx9ZqZJ1w/cyWXagd0wRng539/dPzhvv2heSo1xnVRV/7RqX2UaLvOdSxTEpvG5qjo3ySVV9V+SGBYxtlO6++SqOjbJo5LctruvqqoLF66LBVXVs7v7cVV1QeY16LPjb8hdFyxt0xOEl3NFVT000xp/JyX57ML1sKy3m9nLTjwwya26+11VdUKmb5IY15VV9YAkP57k2UmurqpTkhyybFksqbsfN//0d+QaMjRiOY9I8n1JnpfpUpk/sWw5LOw1SxfA5tTdX5pD8I2TnJjkJUvXxKJenuQ3k7wz0xrCv5rp98djliwK9lcmy8EmNIee05Pcs7v1AA6oqg5NcudM74O7J7lpkucmuaC7L1ywNBZUVU9ZY/fNM02Ye8i+rgf2d4LwQqrq7O5+9NJ1sDkIPazmIitcE1X1xhXzThhUVf1Fd99r6Tr2J8YIL6eq6vbd/c6lC2FT+FR2hJ67ZQo9a/X8MA4XWeEbVNWd1th98yQH7eta2JTeaznOa0aP8EKq6uWZ1gX9q0zXjHcxjYFV1VHZEXrumOToTL2AQg9JXGSFyYqLMW3XSf49yQu7+8MLlMQmMq8acVKS92ZHtrBqxC4Iwgupqpuu3ueXGNvNoef0JP+juw9duh4AOBAZGrGcbwjCSQRhkiTd/d5MX3GZKAfALm0fDlFV39bdn1q6nv2JILyc7ZNeDk9yWpIPJnFRDVbzlQ0Au/OLSV6b5NVJDIW4BgThhXT3b2zfrqonZpokxaCq6mFr7U5y/X1dCwD7na6qpya5WVU9+esOdD91oZr2C4LwQqrqJituXjfJ8UvVwqaws///P96nVQCwP7pfkttlmoT/xhX7fau4GybL7WNVddvufs888/daSb6W5MtJ/qa7X71sdcBmU1XXTvI93b21qh6d5I+7+8tL1wVsPlX12O5+rosyrZ9LLO97z0mS7n5Ukpt096O6+zFJ/uuiVQGb1TlJbj1vH5PkFQvWAmxCVXVoVd09yU2r6t1J3pdpLfIXLFrYfkAQXpbueGB3jurulyVJd/9mpjWmAVb6VJLzMs0tuVuSd3f3U1yZdPeMEd73bjhPjKpV28csWxawSX2sqh6f5B1Jbp/kEwvXA2w+rkS5h4wR3seqaqeXzV25kgRAklTVYUnOSPJdST6Q5KzuvmrZqoDNzJUo108QBgBgSMYIAwAwJGOEATahqnp2dz+uqi7Ijom1laS725WjADaAoREAAAzJ0AgAAIYkCANsQlV1n/nnty1dC8CBShAG2Jx+cf7p0usAe4nJcgCbU1fVU5PcrKqe/HUHup+6UE0ABxRBGGBzul+SE5L8cJILM60YAcAGsmoEwCZWVY/t7ucuXQfAgUgQBgBgSCbLAQAwJGOEATYhV5YD2PsMjQAAYEiGRgAAMCRBGGATq6prV9WJ8/ajq+rQpWsCOFAIwgCb2zlJbj1vH5PkFQvWAnBAEYQBNrejuvtlSdLdv5nk6IXrAThgWDUCYHP7WFU9Psk7ktw+yScWrgfggKFHGGBze2SSLyR5QJIvJvmJRasBOIBYPg1gk6uqLUkOn2/euLvfumQ9AAcKQyMANrGqOjvJzZIclalnuJOcvGhRAAcIQyMANrebJjk9yYeS/GCSry1bDsCBQxAG2NyuSnK3JAcleWCmnmEANoAxwgCbWFUdkeRGSb6S5NFJ3tDdFy1bFcCBQRAG2ISq6oZJHptpXPDvdffnFi4J4IBjaATA5vTHSd6f5DNJXrBsKQAHJqtGAGxOh3b3K5Kkqh6wdDEAByJBGGBz2lJVD0tSSW4wbydJuvt/LVcWwIFDEAbYnF6V5Pg1tk3sANggJssBADAkk+UAABiSIAwAwJAEYYB9qKpeWlWvnrf/pKpeus77PbKqHrnOtrerqtvtcZEAgxCEAfa9284/T9hL57/d/A+AXbBqBMC+9+Wq+rZMl00+sqpen+TaST7c3Y9Kkqq6MMk7k9y2u++5/Y5Vdeskz0tynyRXJ/mjJDdI8t7u/rmqenqS+81tH97dd9t3Twtg/6JHGGDfuzTJg+efVyV5fpJ7JTmuqo6Z25yU5K0rQ3CSGyV5RZKHzZdcPiPJ+7r7zkluVFW37e4nJHlGkmcIwQC7JggD7HvvSvLI+edXkvxUpoB7/SSHz23e191/uup+P5/kY0luOt++ZZL7zb3HN09y471aNcABRhAG2PfeleT288+DkrwmyUOTfH5FmyvXuN9/T/Kz888k+cckz+nuU5M8KclH5v1fTHKdJKmq2uDaAQ4YgjDAvndZkn9K8uFMvbhPSHL+fGxXvbpf6u6PJPlAVf1IkhcnuVdVvSnJzyT56NzuDUnuX1VvTnLKxpcPcGBwZTkAAIakRxgAgCEJwgAADEkQBgBgSIIwAABDEoQBABiSIAwAwJAEYQAAhvR/Afe7fHd9x5P6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#可视化一下\n",
    "market['Sales per customer'].sum().sort_values(ascending=False).plot.bar(figsize=(12,6),title=\"各个超市的销售额度情况\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order Region\n",
       "Western Europe     5.296003e+06\n",
       "Central America    5.093850e+06\n",
       "South America      2.660244e+06\n",
       "Northern Europe    1.939362e+06\n",
       "Southern Europe    1.837526e+06\n",
       "Oceania            1.809997e+06\n",
       "Southeast Asia     1.738553e+06\n",
       "Caribbean          1.481669e+06\n",
       "West of USA        1.412254e+06\n",
       "South Asia         1.397365e+06\n",
       "Eastern Asia       1.334313e+06\n",
       "East of USA        1.231955e+06\n",
       "West Asia          1.056081e+06\n",
       "US Center          1.034129e+06\n",
       "South of  USA      7.069043e+05\n",
       "Eastern Europe     6.963072e+05\n",
       "West Africa        6.541680e+05\n",
       "North Africa       5.722420e+05\n",
       "East Africa        3.380543e+05\n",
       "Central Africa     2.929126e+05\n",
       "Southern Africa    2.043025e+05\n",
       "Canada             1.682574e+05\n",
       "Central Asia       9.795369e+04\n",
       "Name: Sales per customer, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#同理可以做一下不同的 Order Region的分布调查\n",
    "region=data.groupby('Order Region')#按照‘Market’进行聚合一下\n",
    "region['Sales per customer'].sum().sort_values(ascending=False)#可以看到这里是 有这么多的 区域Region  然后也按照地区排序了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'按照地区销售额的分布情况'}, xlabel='Order Region'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#同样可视化一下\n",
    "region['Sales per customer'].sum().sort_values(ascending=False).plot.bar(figsize=(12,6),title=\"按照地区销售额的分布情况\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category Name\n",
       "Fishing                 6.226935e+06\n",
       "Cleats                  3.982857e+06\n",
       "Camping & Hiking        3.700784e+06\n",
       "Cardio Equipment        3.320251e+06\n",
       "Women's Apparel         2.828708e+06\n",
       "Water Sports            2.798044e+06\n",
       "Men's Footwear          2.598494e+06\n",
       "Indoor/Outdoor Games    2.596454e+06\n",
       "Shop By Sport           1.177186e+06\n",
       "Computers               5.953950e+05\n",
       "Electronics             3.333273e+05\n",
       "Cameras                 2.404967e+05\n",
       "Garden                  2.317655e+05\n",
       "Children's Clothing     2.092684e+05\n",
       "Crafts                  2.007049e+05\n",
       "Girls' Apparel          1.362068e+05\n",
       "Women's Clothing        1.260069e+05\n",
       "Accessories             1.197125e+05\n",
       "Sporting Goods          1.050636e+05\n",
       "Golf Gloves             1.047874e+05\n",
       "Music                   1.016873e+05\n",
       "Consumer Electronics    9.793756e+04\n",
       "Golf Shoes              9.650647e+04\n",
       "Health and Beauty       9.535811e+04\n",
       "Kids' Golf Clubs        8.947370e+04\n",
       "Baseball & Softball     8.436727e+04\n",
       "Boxing & MMA            7.628672e+04\n",
       "DVDs                    7.131938e+04\n",
       "Golf Balls              6.934125e+04\n",
       "Trade-In                6.166754e+04\n",
       "Hunting & Shooting      5.087891e+04\n",
       "Strength Training       4.881384e+04\n",
       "Hockey                  4.361836e+04\n",
       "Men's Golf Clubs        4.238279e+04\n",
       "Tennis & Racquet        4.007781e+04\n",
       "Women's Golf Clubs      3.998439e+04\n",
       "Men's Clothing          3.936571e+04\n",
       "Pet Supplies            3.731830e+04\n",
       "Lacrosse                3.541593e+04\n",
       "Fitness Accessories     3.175136e+04\n",
       "Golf Apparel            3.144744e+04\n",
       "Video Games             2.991920e+04\n",
       "Basketball              2.470533e+04\n",
       "Soccer                  2.390995e+04\n",
       "As Seen on  TV!         1.851961e+04\n",
       "Books                   1.130342e+04\n",
       "Baby                    1.095740e+04\n",
       "Golf Bags & Carts       9.403790e+03\n",
       "Toys                    5.485680e+03\n",
       "CDs                     2.750030e+03\n",
       "Name: Sales per customer, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Category Name这个字段表示的是商品类别的名称 然后看看 按照商品类别 销售的额度分布情况\n",
    "category=data.groupby('Category Name')\n",
    "category['Sales per customer'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'按照商品类别销售额的分布情况'}, xlabel='Category Name'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zMzOzTllhnE++6SFnTvv7Gw/fY5yrNTMzM7NlmK/gmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKY0DXEkfl/SMcXbGzMzMzGxUjQJcSU8C1o+IM8bcHzMzMzOzkdQGuJJWBD4N3Chpz/F3yczMzMysXJMruC8Efgi8D3icpP8cb5fMzMzMzMo1CXAfDRwdETcDJwE7Vf8o6QBJV0q68tZbbx1HH83MzMzMGmsS4N4APDD/vDVwU/WPEXF0RGwdEVsvXLhwpvtnZmZmZtbKCg2WORY4TtKzgRWBZ463S2ZmZmZm5WoD3Ij4P2DfCfTFzMzMzGxknujBzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXXK0ABX0gqS/lfS4vz1yEl1zMzMzMysxAo1f98S+HxEvGESnTEzMzMzG1VdisK2wN6SLpZ0sqS6gNjMzMzMbFbVBbhXADtExHbAn4Hdx94jMzMzM7MR1AW410XEb/PPPwIe0r+ApAMkXSnpyltvvXXGO2hmZmZm1kZdgHuipK0kLQ/sDXy3f4GIODoito6IrRcuXDiWTpqZmZmZNVWXU/tO4HOAgK9FxDfH3yUzMzMzs3JDA9yI+D5pJAUzMzMzs3nBEz2YmZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05pFOBKWk/SNePujJmZmZnZqJpewT0CWGWcHTEzMzMzmwm1Aa6kJwN/A24ef3fMzMzMzEYzNMCVtBLwVuCQIcscIOlKSVfeeuutM90/MzMzM7NWVqj5+yHAURHxZ0nTLhARRwNHA2y99dYxaoc2PeTMgX+78fA9Rn16MzMzM+u4uhSFpwAHSloMPErSMePvkpmZmZlZuaFXcCNi+97PkhZHxMvG3yUzMzMzs3KNx8GNiB3H2A8zMzMzsxnhiR7MzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnrDDbHZgJmx5y5sC/3Xj4HhPsiZmZmZnNNl/BNTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmadssJsd2A2bXrImQP/duPhe0ywJ2ZmZmY2U3wF18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrlEYBrqS1JT1V0jrj7pCZmZmZ2ShqA1xJGwBnAo8DFklaOPZemZmZmZkVWqHBMlsA/xURl0taC3gMcM54u2VmZmZmVqb2Cm5EfDMHt9uTruJeNv5umZmZmZmVaZqDK2A/4G7gn31/O0DSlZKuvPXWW8fQRTMzMzOz5hoFuJEcCFwKPL3vb0dHxNYRsfXChU7PNTMzM7PZ1aTI7A2SXpgfrgn8eZwdMjMzMzMbRZMruEcDL5B0EbA8cO54u2RmZmZmVq52FIWI+BPw1An0xczMzMxsZJ7JzMzMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKU2m6rWKTQ85c+Dfbjx8jwn2xMzMzMym4wB3QgYFxg6KzczMzGaWUxTMzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUzyT2RxWMi2wpxI2MzOzZZ2v4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOWaFuAUlrAKfkZf8K7BcRd427Y2ZmZmZmJZpcwX0ecGREPBW4GdhtvF0yMzMzMytXewU3Ij5eebgQ+N34umNmZmZmNprGObiSHg+sFRGX9/3+AElXSrry1ltvnfEOmpmZmZm10SjAlbQ28FHgpf1/i4ijI2LriNh64cKFM90/MzMzM7NWagNcSSsBpwKHRsRN4++SmZmZmVm5Jldw9wceC7xJ0mJJ+425T2ZmZmZmxZoUmX0C+MQE+mJmZmZmNjJP9GBmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad4gDXzMzMzDrFAa6ZmZmZdYoDXDMzMzPrlBVmuwM2N2x6yJkD/3bj4XtMsCdmZmZmo/EVXDMzMzPrFAe4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOaRTgSlpP0rfG3RkzMzMzs1HVBriS1gI+AywYf3fMzMzMzEbT5AruP4H9gNvG3BczMzMzs5HVBrgRcVtE/GXQ3yUdIOlKSVfeeuutM9s7MzMzM7OWRi4yi4ijI2LriNh64cKFM9EnMzMzM7NiHkXBzMzMzDrFAa6ZmZmZdUrjADcidhxjP8zMzMzMZoSv4JqZmZlZpzjANTMzM7NOcYBrZmZmZp3iANfMzMzMOsUBrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKSvMdgds/tr0kDMH/u3Gw/eYYE/MzMzMpvgKrpmZmZl1igNcMzMzM+sUB7hmZmZm1ikOcM3MzMysUxzgmpmZmVmnOMA1MzMzs05xgGtmZmZmneIA18zMzMw6xQGumZmZmXWKA1wzMzMz6xQHuGZmZmbWKQ5wzczMzKxTVpjtDtiyZ9NDzpz29zcevseEe2JmZmZd5Cu4ZmZmZtYpDnDNzMzMrFMc4JqZmZlZpzjANTMzM7NOcZGZzQuDCtNgcHFaSRszMzOb/xzgmvXxKA9mZmbzmwNcsxngq8VmZmZzh3NwzczMzKxTHOCamZmZWac4RcFsFjnf18zMbOY5wDWbZzyihJmZ2XAOcM1soJIrzKXB9KTWNdf7Z2Zmo3OAa2Y2h8x0AD6s3SSDfTOzSWoU4Eo6Fng4cFZEvHu8XTIzs66ZCwF4XTsz647aAFfSPsDyEfEESR+X9JCI+OkE+mZmZjYRcz11Za73b5Lr8p0Ha6LJFdwdgVPzzxcA2wEOcM3MzKzTHOyPvq7ZoogYvkBKT/hIRHxX0i7AYyLi8MrfDwAOyA8fBvx4wFOtA/y+Zf9K2nR1XXO9f5Nc11zv3yTX5f7Nn3XN9f5Ncl1zvX+TXNdc798k1+X+zZ91zZX+bRIRC6f9S0QM/QI+DGybf94HeGNdmwHPc+Uk2nR1XXO9f94W3hbzsX/eFt4Ws72uud4/b4v50z9viyW/msxkdhUpLQFgK+DGBm3MzMzMzGZFkxzcrwLfknR/4GnAtmPtkZmZmZnZCGqv4EbEbaRCs8uBnSLiL4XrOnpCbbq6rrnev0mua673b5Lrcv/mz7rmev8mua653r9Jrmuu92+S63L/5s+65nr/6ovMzMzMzMzmkyY5uGZmZmZm84YDXDMzMzPrlM4EuJIO6Xu8uaTtWz7HA2e2V0s89+qSHiBpDUkvkbTRuNZVWedmkhq9xpK26Hv8mvH0au6biX1pnCS9tO/xBpIePqZ1td5vJS0n6b6SVpC0k6TVx9G3ada7fItlW/dR0uMK+jTRbZHX90RJ75L0nTGvZyL/V8n7cZLv4Zk4tkvarn6p4v22aFtI2igv+wBJ75D06CZ9bGsWjxeNz4+WzEYcM4qxv7iFB6eSHX5LSZdLenZ+/BbgdTXrObHvVyc1WE+pLwMbAR8CHgx8oa6BpLPbrkTSJyT9u6R3ACcyNQtdnY/2Pd6r4fpGOjiN+yAjaV1Jz5b0wt5Xg2at96W8rsbbQtLGg74a9G9HSadL2ik/Phx470z2r6L1fkva57YFPgi8DPhKgzZIWlnS1vnn/SWt1KDNmyQ9S9KrgZ9JOqrJugr7+EpJl0k6VNIDxrieVnIg8jJJpwE/Ak4DfgE8q+C5mn5IKH2NJ/V+LH0Pt/4QQ9mx/by+Xx3WcF0l271oWwCfAwR8APgVDQt9JD1C0q6SHi5ptQZNWv1Poxw7Rzg/WlJyPpi1DzFNhgkb1ZaSLgc+FBGnkN5cC4CLhrQ5lfRmegawNvAm4Ck163kgabzeC4FTgHWBu6ZbML8RNgO2qATbC4C7h61A0iKgvypPQETEk2v6t2JEXCbpbRHxEkkX1ywP8D1Je0bE6Q2W7dkiIl4l6YCI2E7SpcMWlrQnKZj9F0nH5V8vIJ0gm2j9Wkn6BPBNYEtgZ+Bm4JkN19fWN0gH6ltbtGm8L/Vpsy3eMeD3Abx0wN96HkqadOUUYBGw4Rj611Oy364TEedKOjgidpN0SYM2vf59CbgSWA84Gdi3ps3uEfFESeeQXreh+/sofYyIl0paEdgDWCTpf4HDI6I/WClez7AP/xEx6Jh5DbA6cBTwH8DnIuK4ActOt843kaZfXwd4vaQzI+LAmmalr/Gk3o+l7+FXSvow8DXgxIj4VYM2jd8jkrYEHg1sWAnuFwB/b7AeKNvupdsC4Hpg9Yj4tKQX1S0s6aPA/Unn17eQPnj/W02ztv/TKMfOVufHUUg6OyKe1mC5n7JkXNGLKR4q6YIGscUklZwPoOzcM7JJBLglb66SN/EfSQf4lXPQ9lAGTxu8GWnos7XydwF3UPPmiIidhv29xi8lXQN8RtILgN80aLMN8J+Svgf8jWaB9D8kfQj4ab4aMTRoBxYD3wX+hakDxx0R8bsG/YOy12piBxngtog4omWbNvtSVeNtEREvadmnqjuBFwGrSdqKtD/fMJP9qyjZb/9P0leBqyTtDvxfgzYAa0XEZwAi4j35A2WdeyS9NvdrM+CfDdfVuo+S/hV4HvB44Iv562hgWIDbdj2DjjHB4IsCC4HHAbuSrt4+Ih8DLoiIr9WsD8o+JJS+xpN6Pxa9hws/xLR5j2ia73+g+dX2ku1eejy7FPgZ8Ob8HvtugzaPjIgdc2B2pqTXN2jT6n8a8djZ6vw44kWtRheoIuIhQ/78nJp1TFrJ+QDKPxCPZOzDhEk6C/glsDXwTuBjwI8jYmD0LukM0onqB8AlwEERsVvNelYmBWm/BnYjXdXYNiKOGdLmuIio+8Q3YyStHRF/VLq9eXNE/GMM61gIPAk4m3Qi/llE3NSg3bMiovXtmsLX6gLgOtIHrM8C74+IHdquu2H/3g08KK/nbzD0SlivTet9KbdrvS1KSFoXeALwc2BP0myD20TEoCsbI/Wv7X6bt9/mEXF1DsBvbDJ+tqTPA9cC3yF9uHtsROxX0+ZhpGDkRNKH1Z9HxFUN1tW6j5JOIO1HiyIfOCU9OSIumMn1jErSmqSrI7tExAENlv8W6cr5VsC7gc9GxBNr2pS+xhN5P47wHq5+iDmP/CEmIrapadf2PfKeiHjjsGUGtCvZb4u2Rd9zrBIRdzRY7ovA94C9Sbexd2/wHp7Ye6T0/Fi4rkWk1Is2F6jmvJI4ZlLnxqXWO4EAt/Tg1PZNvC7wZGAlpj5hfbZB3x6V2wD1B9vcTqST6nrAD4GbImLoJxlJ94uIP1QeNwoo8xtylfxww4i4rEGbtUi3if4I3BIR9zRos26Lq7bVdiWv1UgHGUmbkbZ5k//rbX2/ioh4Z02b1vtSbleyLVYnXY29Hymw+2VEfL+mzX1Itzmr/Wuy3zbun4bkRjbcFo8gpU78L+l/+muDNvcBDiAdL35ECizubNBu1Yi4XdKDIuJndcuP0se+9g+MiJ83WG5LUlB3Y0Rc02YdDfsxylWm4g8JJSb1fhzhPXwC7T/EiHTr9UGkqexPb3hsKtr/2u5PI2yLkmPTKqT38MNI7+FjIuL2BusqOV607l9u1/r8OG6SjiedD8+LiD8VtF8OWA24nXRuvTIihl4Jz1fXT6qLXfralMYxI32IaXPOX6LdBALcojdX33NsFxFDcz0kXU1fblfk251D2lxMumLUa1N7sM3tTiVdlX4S8F/Au+pOJOrLpZF0Yd1VS0nHkm67rkXacSMihlbbSnoD6dPzqqT8p10joraQQ9K1EfGouuVmiqRNIuImSTtHxPkNll8qbzciGuXttj14luxLA56nyX57NnAW6VbUy0lBXd3Vs6L9NrdttC00lW/3XOAmUl7so4GHxJC7L7ltfx7eKyKiLg+viKTDSMWyhwDnABc3fA+37qOkEyPiBZXHl0bEE2rafADYAriatO/+PCIOqutfG5IOz8+9IumW4dX565oG+/rapKtMa5JO+JeN+wpzW4XH9uL3cNsASNLngNuYeo3XjYihKQel75GS/al0WxQem3qB1h2k1MQmgVbptijpX9H5sVSL4+0zSOe1nUhXe88l5at/u/dBq2Y9p7Fkjut6DY7TLyTVOIi0f3wlaq7Sl8QxeblnAV+NiKa53yOd8+8VEWP9Ir0RX0f6pPUi4EUN2pzX9/hbDdosLujbRYX/06L8/YK6dQM7AG8j3U5+a/56b36x69bzTdIHgy+SbufX9pd0gq/28eKG/9OzgfcAq7bcFmcXbL/jgdfln48GPtX0tQLOyd8vbbiuj5Juv15Nukr1tXHsS7ldyX77rab7Uv+2KOhfybY4v+/xBU23X+V/urCkvw3/p8tLtk2bPgIb5/fx1cD2+etpTf6v/vdf3fux8vxLfTVY10OBVwHfJgUY36lZ/hmkIO5sUkHf2aSCzx2HtFkEXND3tajJfjHCa7x4Em1yuzeQpqW/jpSq8Nm262r4Hm68/42yP424LUqOTacBu+RjzcnAN8e4LUr6V3R+LNx+rY+3ud2GwEdItUp/aNimtw2/kb9f0qKf25NyrW8C3jJgmeI4Jrd/Kymn+1PAExu2KTrnV78mUWTWuKhAo1WYXqyUw9c4tws4L1/9+Eylzf82WNdPlUYc2CDfcvvJkGVvJBVy7ZW/9wramtyqvJP0yWV50iettRq0uS1vu5Ul7QD8uUEbgFfk72emO25ENMsXKhnp4aGRCwUi4gA1KyZqWzzXU1L00GpfGnG/PV/S+cAD822qbzVoU7rflmyLv0g6knTC34JmRS23SnorsFa+EnzzsIUlHRkRB/fdam96i/2veX+4kpTn3zSvvU0fWxelamrIol9Jej4p6HxszXqA9kVmSuPdbk461lxNKuZ9M/XFvG8GHhMRN/b1+2TS3amlO1FYaKulK8Vh6jV+aE3zkmN7SRuAZ0TEtpIWRcTJkl41aEFNjXjxF0lvZuo1/luD9bR9j5TuT1C+LUqOTSXFRK22xYj9a3V+1GjpP42Pt5IeTMqbfwopbfJq4JWku1JNlBTNHkIa4eJnpA/G55OOMe+aZvEbKY9jiHRX7Z15m39W0j3A/4uIE4Y0Kz3n32sSAW6bN9coFaZ3k3J+euMYDjwhVPQmdujteEH9MCO9oGzPvL4fk4rnBi17E3CTpOMbHlSqngVsQEqD2B94dYM2LwYOBf5EKkBqVERXeuKibKSHP0raj6liotocLWA/lszbbXpbqeTg2XZfKt5vI+LtSjlJDyMVXzapVC7abynbFs8iDUm2KSmIfFODNi8k5eFdBqwBDK16joiD8/eSffAA4P2kvN3rSbcqm2jcx4i4ELgwp9U0SgWhMiIJ6UPqk0n7xW3DGkVNoeAAZ+YvSPvC6qT3CKTRawa5B7hT0v0rv/sH6QP1jIrhleJ1So7tJW2gXQDU21+vJm2zx5Ne4283WE///vfimuWL9qesaFsUHptKRnlodbwYsX8vpsX5cYTzIrQ73p5ISkn4ACktoW1e8L4smeM6tLAvu5lUiHpv2oSkp0+34IhxDPl8/1xS+sp7SVe2zwJOGNKs9Jw/td586XdsVFZUUFRhWqKSb/UnUr5V02GGJqJpDk9l+YNInwB7wVZEg5EilGYkeR8pUPgBcGhE/HKErg9b1/2ANzIVlBweEb9v+RwbRMRvGyxXVPRQomS/VWFxX4mSbSFpDVJ+68OB75NGvKgrnHsmcGY0qLqeLZJWIH1o7P1fJ0R95fsO9F3NqTvY5zb3Lt6kTVvTXGWqvvcHftistFPfn5revekcpZqRQ5l6j7w3Im6pafMiprZj7zWuK4Lrnawbt8ntxr4/jUKTHRGh9bGz9PxYos3xVtJeEfHVEda1ICL+Vnn89Ij4ek2bouLIwv69HfhMRPyi8rvNI+KHA5Z/BCl1Yk1SGtUFETHsTvn06x13gFtKBaMHFKyjmnB+OLBbjKkgq7BNSSHMZaRPSvcG6k1uXyvNrPMu0tWHxwNvioinNuznSNXoDdfxbtKbsTczzt8iYsshy5cMmF/atz0j4nT1VZg2bNt6v5gkSaeT8uq+QypG2ici9qxp8wZSztafSQPmn1E9+A5p17oSuJTSLIY/Yer/enBUCsgGtOl9WF8FeCrw04h49pAmvTZBOsY0apPbPRD4d5Y8Br5iwLJFRWa5b2cDV8QETwSS3hwR757U+tqQtHz1IoekLSPiupo2L8o/rkIaKej3EfGymW6T2xXtT5My4fdwyTm19Py4PPAYlnw/fr6mzcrAIyLiSkn7kyYOGTT51EgTOkj6JqnYbjPSefz6iHhtTZuS4sii85XS2NIvIV1Q+AFDLihIehkp3/dM0p3QtUnvkddHxBdbrXcuBrhqMXqARsjfk3RxpIkGFkXETr3HDfr3WuCeiPhIi/+ppM3imMrhebKajbzwVeCRpITxNkMFXRKVCtT+x0PaTaRiPr++TweOIU3n+LWI2HnI8r1gZAfSLbqrSJ/cV4+IafMLS/elyuvT+iClNH3mlsC7G1xNfX1EvE8p36y/f+O6CnFRRGw/6HFN29WB1wKviYh1GizfuBJY0gsj4rOVE/696u4Q5faLonL7sfdeq2tXWX554KiIeOU42khaTMqF3ZVUyPXkqKkglvRQ0u3rF5P2qe9FxMCpZyX9Z17+kaRA/xxSkcrAW6nTXC2GFseZ/ByN3icl78dRzge5/VdIgcI6pDGB7xsR+9T/V0s8x8cjokk62ahthu5PIxzPRjmntnkPj/paNT52Vtp8lbLz41dI6RabkT5ErhURu9S0+RrwpYj4jKQ3Ao+OiGlnZpR0O2k65CV+TbMcdSRtTjpe/BJ4dTSYga//mNfkGFgSx+R2J5JmS/w2NRcUlGoKdouIP1Z+tyZwVtSMWtNvbDm4I+68m5Ai9pNJB5uBYxDGaPl7pQVZe5IKzJ5Lyolq8j+VtCnJmVyRlNze9jb8GUrDrlxByov9WsN2bRLpRw3QtiJdHdiSNHvTQJFzGSWdHxG7Vvowjn0pJL0T2Cy/XtXnrAu22hT39Yb2eXubzo34XrxW0qeYurJ/bYP1/RuwO+mDzxUMKFiaRpsClV6+3eKGz93vt5IOZer/qh0LUkvOdb86UJtXWtImW4n0YW6PiPi4pOcNWUd/kdnnaVBkFhEfBT6ar7ptQxo14JOSfhwRWw1oM0pOYisl78cRzwcAHyblp/6ZdBfriroGWvJu0eqkYswZb5PbNd6fSrfFiNuwzWyOo75WJYXRpefHNUjTyZ8aEfspTY5Sp83MjN8u2Q5acrzyL5Cm6t5d0t9jQMqLRiuOLIljADaqBLTn5A/wg6wI/Et+Tavu02A9SxhbgDvizlsyekCJFzOhgqzC7VCSfL8+cIWke/PGmuyAEXG4pK+TbiF8ISJ+0LCPbYLwogAtey4pDeItwMGkqytN3KOUd3UdKQgYh71JwfczmKowbaTlyfuW/P2mNp0b5b0YEQdJ2oO0X3w5Is5u0GwL4IiIaDJ9cFXjApXIBSWRCsBKvJhUkLYP6ZbZi4YunVQLwO6i2T5YbXNnwzaQ0kIOBH4i6VyGj8hRLTKDdKx4AunDzMDto5QjuAOpcvvJpP/pfTSv3C7V+P0xKZWT/j2kwpcDSce07aM+pan6vrqLZsXA1TZ3NmwD5fvTpJRO4dxa4Tm16PwI/JZUcHtn/mB83wZtfqWUrvUdUoHfsHzh0xo833Sq76XfMFUEPOw91l8c2bsq+p26lY3wYeQ3lQsK2zL8gsK1pLu0wL056iKdw1uZqykKC0ijB9xNKgQ5LyKGfmJSWZJ1aUFWST7OGqQxFns5KE2Kdc4k3Vq/Crg6GhZ9KRVKVPtWO7d8vp38MlK1/A3AcdEsb3JiRVwl8q2NA0i3lm4CPh01ebJacnISoHEByEEFt27WJOXSVV+vw2raVGcy6/VvLIUmKisya5xv1deudPrX9VlyWzTJqVuX9CG61y6avMZ9z9Go0HFUSvm4v4oWg6Q3fN4bSceWa0gnkT/QophDhXUSkj4ZLVI7pmnfZAKVVu9hLV0MXWnWeOSMxjRNwWJe2Yy/j0uPZ4XrmmSR2Zq0PHbmdiXnx+VIM6bdTfpQfElE/LimTXVmxutJ557amRlL5GPuS5kqEm96zG31Hm4b+2iqPmUl0gWFzXP/jhl0PMvx3wHAT/Jd4deRUlWPj5aFy5OeyQxoHCy0HT2gJMm6NOG8JB/ndNLQGL1bontHfbHO+qRBs/cl5eLdEhEb1bQ5lhSkrk3D2c9yu7NJ4+BdRwqeto+IPeraTfM8TU4+rQM0ScdGxP5t+1NCo82C1PaAcR5Tt4i+Bzw8Ip5R06Z4JrO2+vbbpkVmjfOt+totVeBZd6zIV4tWYMlt0eRDasnsWO8iXaVfnRSc3B5DCh1HIWkD0jBwDySlHrw/GuTVtVxHcTGHCmZZrLR9JCl9pemx/byoFLxK+lYMyKOvLFM6e5eAjaPFLIsl8nv4L6RUm8eSjoWLxhRMt9oWfWkQS6g7PyqNTvJSWny4lXR2RDxt2DID2pUcO0vPj61HHFCLIrNRqaxotmSm1Faxj8rqUr5EmsntnIi4UdI2pDtN20XEXm2eaxLj4H6DvjdXHS1duPRe0oDEwxxE2ii/BPZveDK4hTRb2L0J56RgvE5pPk7vZP1jSU3SIa4lvYHPIP1/NzZoswlplqXa/OU+94mpCTnOlbTr0KWz/pMPcBj1OZfn0xegUT8uoyRtEw1y4voalRw8G09O0reupQ4YpOkqh1mJtI9/OSJen098de6JiP8o6F/Jtujfb5t8yGiTb7VEF/P3eyvLSeNnD7MwGhRDTqPkNd4OeCKVQsdBC2r0YqzPA58EPkFKf/kcadicmXQAsGVE/OneDuZiDtLsicM0rpOoanNs12gTqBS9h4HjSIHZEcB+kvYddLV5xNf47uoFBKVagYHB7Yjrarst3jHg90F9Gt/xpA+33yAFWscDdR9uSyYLgrJjZ+n58WSmRhzYObetG+f8VNLFgSuB9fJzTFtkNgP6j7lNUrdK3sNtY59tJf2EqfgKqC2eWz8iPtV7kM/5V0jau0H/ljCnZjKraFO41DrJuqI04bwkH6darLMtDYp1SNWeu+Z1HUG64vGImjbF+cuSPkm6Zfk44G7lSvUBy45y8ikJ0FYCvinpHKYmlGjyIaHxwVNTeXilM/+UHDCuIhVL/D7vHwMT6StXVkpnMis5kbQuMqNdvtW9+q4qfVLSxxs0+4qkV5LGWKy9fTUDr3GjQscYvRgrIuKU/PNPJLWqsG+oV8zRn6/XpJij9DjTZjY9TfO96cQ/pa9v41kWR3yN78mv6Q9I+9LQq4El6yrd13v/f6EHFHy4LZksCFocOytK99v7R8Rzew8a/l9tisxGVT3mPh74dYM2JduibexTUjx3vlIx+FmkMXBXI6WiXNnyecaXolB5c+1Cuqzf+EAj6YukWw57Ax8Cdo+IaWfm0NR4gktpcMvxKmBl0pXcXpsmQ4a0zsfJ7fYg56BExFkNlv8Oae7na/LX1VEzIYKWzl8+ty5lILebdjsO2oZK+VWPIuVnHgb3Tt13XvWK0IC2byEFw40DNEmbTNO32mKrfFDZlrQ/DT14asQ8PKWc6Y+Rco2+CLwxIh7ZoN2K+ccnAdcNeo2VRp8Y1L8mt+Ubb4u+dr0isx9EgyIztci36mvXX1n+P1E/bE3vpNErRBj6P43yGue0gQ1Js30dDHw9Ik4d1qYtTQ17tlNez3dIt2AX1N2WL1jXoP2pNsiZ5jhTWyeR2zU+tlfalEyg0v86N30PnwGcxFRh0AujIFWrwXruR7qCvjFpqtRjIuLPM7yOieYV53V+jvT69j7cPqIaGI5hfY2OnZXlW50fK8ek/yaNCNNLiXhS3X6RP1Rcy9S+9Ji6fb1U5ZjbSw05tu6YW/Iebhv7SDowIo5q87/kdo8nXdxbj3Tl/JKIaDqy09TzjDHAHeVEMsnZpx5Dysn5aUR8r2Gb5UijLmwK3BARZ4yjzTTPMTC/VSn3aV9gEWnHW5OpE/57hjznSqSTzFfzdv8f0ifhD0eDWWIKTz69E2s1KGkSoK1Fur35R1I+8lhmXZlmvbV5xXm5kgNGUXFf33M8MCJ+3qZNg+dcizQX+udIV0kWwr0zJx0ww+s6rv9XpNz406Nm4PHc/l9IxRU/jAYFUpIeGxFX5ffk80n7+4kRUXv3QUvOfHjzsH1Q0l7ArRFxidIwX3+q+2CrJWeqWkI0GDFCqSjwpaR8vCdFxCF1bdrIV3t3IX1Auoz0AXd10nistUWwJcd2FRYtltDULIu9/g2cZVHSqqT/5afRshhGBfmZJfvTgOdpejxrfWwq+XCrgrzdtv0b4fw4KI65dyjKIW17RWa9fWmcRWaNZ2YseQ8r1QMdQpq44yRJR5I+gH8gamb6m02TKDJbjrTxbiflsI1tZpO2JH2ElAv2PdLVyB9GxOsatDuVNORHryBrjbpPqbnNLXldTds0Lq5QGmD7+6QriGeQhuLaCnhqDJmRLF+x+HZEvDt/+v7f/H+9KCrjxw5p/yL6csOiWRHhruQ3Y0R8s8Hy1Vnn3gvsGg1mncttWxd+Nd3uo1JBcZ+kE6NSQCDp0mgwAHabE0nu1xeYmjP8ZaT3yPNihifykHQK6Tbt3aSim6tIuW43Nbiy/9+kT/pXkW51nhURRw5Z/jPAnZFuP3+IFLj/ANgmIobmeKnFzIf5Q9wtwCkRca2kPUm32VaPiNohySRtEZWh+iS9JiI+PGDZvUi1AyeStt9/kd7/H2z74bNBvz5PGiN2HdKHuStIwf5Th71HNMLMgiosWizV9NikVAxzHqkY5hdqUQyjFpMA5OWL96fS41nJsalE6evbpn+l58ch625STN16OvBSalFkVvIeztv6w8DlEfFnSQ/I63lFyfabmIgY6xdpfLddgI+SchO/OcPPf2T+voiU83hB7+cGbS8e9nhIu8V9jxfNZBvSyf5FpKFFXpi/XkW6IjiozbcqPz+/8vOlNf1alL8/BrhsUH+HtH9R/nol8FXSJ/ba1ww4lnRl8HjSp8BGr1Wlv01fq2PzPnENcMmwdqQD3ouAHzbd7n3tzy7Yfy8Y9rjvbxuTTqBXkwqOticVTFzYcF0nAm8lBYRvI101GrTsJZWfn5K/rwB8Z9i+VHkPtnov5vZr5+39bVJO4vVN94v8s6r9rtnfH0w6MfY+5Dc+XjTZB4GL2vy+wX6xaMiy3yXl5p+UX9+L8v/XaL8o3V9Jt4SXeh0GtHtb/rqANM7ue0gfnL7VYJ2Lhz3u+1vx+aDXvu/YdOSQZafd1+r2wbzMt/oeD3x9S/enGTieNT42VZYpOQYu6ns88PUt7R+F58fKcucNer4hbU4kFcHVHm9H/WqzDUvew6Qrvcv3/W45UsA7lv9pJr4mUWTWeGYTTVWKVgf3hSF5dTHahBK3SNqPFDA8DvilpI2jvmDndkmHMFWQ9RfVDwjepk1/0UeT4oqrlYZMWkyaoe01pDfWNTX/y42SPka6AnRgvkV3IA0H6I6ywqDHxtSUw5+S1KT4o3TWuU1oX/hVUtQChdXAal7ctxmwI6kYYMfcvztoOEEJ7QpAvq6Uo/4t0rbfg7SPDMw5LXwPIukLpA91d5GCtc+Thsi6sUHzv0valqkrF3VpBn/Kt5L3Jg0nuEDSPg272mYfvD5fdTuTJQslho7ukq/M7UUq/uqlbiwAfjGk2YWk0RbuJN0OfSOwLqm+YKatqZQfJ+Af+eflmLpDMq0omFmwor+AZmDR4ojnA2h3bBqlGKY6CcA2DJ8EAAr3p6z0eNbm2NRTcgxsPatgQf+Kzo+aqjUpKabuP942GdmgVJttWPIe/ixwXY7RevvfTsApQ9rMukmkKJxBGmf2B6QraAdFxG5jXWlDmr7IIqImH3RAXk7E8GFeStq0ym+VtDMp8FlIGmOxNjE7p5A8FfhtRFwnaSNgD9KnzSYTPfQXBr2+coIY1OYMUm5n75bUc6J+/MJ1SbPOVXPjmuQI9xd+HRo1Y5e23e6VdouYKuL6K9QXLaplcV9uc1zdPjqgXbUA5PHAFjEkTUbSg1hyf7o08gxiM2nA+xCavRc3Bd7PVNrFGyLixiHLr0oatujXEfF1SQ8mpV98OGombZhmH3xvDMg/y++r/egrlACOjiG5vko5tGuRgvxn51/f0WRfz+3XIY24skru39VN2jU15LUiGlTfK41degZTMws+s8F7pHUBTal8bDqZqQ9Mz44hKTkqLIbRkpMA/Ii0XwzMzyzdn3Lb0uNZybGpegxsVMiqJfN2v0/D17dt/wrPj70At6SYutXxdhRt3iOl7+GclvAUpva/sZwPZtIkAtzGM5uoIGlfU1XHSxkWPI5KqThtE1LB2NDiNBUWtWiCxRWl+gL3u4CvRc00v5LWJgUKvQPa4Q0OFq1mndNUUcHFpHE21yRdsXtrRAyd3rLtdq/stzfkoKlNsclypCt2vX2pUfGhpF2YGtmgNoc5tyk6kTSl6Qs5gDRMzkytZzZIehgp0PxfSc8nXcE4OcZUTyDpWTHDIzTMBSqbWbAol1Etcu8rV+fWIR2bViEFaIdFxIfq1lWiTf9GXE/pzIJFx6aC/i1Hej/dQYs6nUn1L6+r7cWm3uQTrwReQxox5IS6/0vtZ+DrLxg7lLQtGxV9dt2cmqpXBUn7StWkPW8k5XYBEBEnj6mfjYvTJH0W+HuUFbVMtLiiVJtgf4R1tJp1TksXFbyNVIjwlKhJim+73Sv77Tei5cwrKis+PJIUPPb696ferdmadkUnkqam2eZvZ4RCjobrfFdEvGUcz11Zx4dIV23XIE0kswLpCtAGUTADU8263hYR79AYZ9GabW0DO01gliZJb6o83J+Uhws1Vfal2vZvxHWVFnGVHJtKRoc4DTiaNFPY2sB6EfGUcfRvFGo4u2rfOf/DpHP+92l2zm8761xR0ecyI+ZAInDvixGS9qMvebpmudfn78eTZq45rvdzw/aNi9MYrahl8bDHc+EL+AgpmOkVjTQpGDu2YD1fJY0X2ahohBGKCtpu91H222nWtahBmwv7HjctXBp3wedIhRyF6/wysNm4nr/6GpEGRP9J5feXjWFdF1S/d+2LFkWflTaLpns9atp8k3QV7IukDySN3iOT2vaj9K9gXYuHPW7RblGDNl8jjcAD6YLTF5uuh3SBoNFxs7R/I2zDj5JGlLmalML3tSHLLsrfRz7nN1i+qOhzWfkaW5GZpCMj4mAtOcVg3UDsrZP2JVU/sa1ffRwRnxvQrPeJ6O21/8j02hSnjVLU0ri4YhY9JipXHtRsukSp/bS7bWedKy26g/YzcY1SbNJffHib6gsWb5P0nEr/pk35mUbjgs9Co2zzUjcCF+UrGb2855lOTVpRaZIHAbdWfh7HOMy/UCpseoSmCrBqJ7CYR0qKPkuKkFrN0iSpOszeGtXHEXFpg/W1VTzjZIGimQUpK6Yumb3r//Jx4ypJu9OwwHma/jU5dpZqMwPfKOf8tjPwFRV9LivmVIoCtE/a1+zM1tK4OE2jFbVMrLiiVL49/0WmDjJPJxX6DEsfOCkvdy4Np91VwaxzKigqyO1KBisvLTYpKT5sncOc24294LN0m4+wvqUKGqPBhAgt19H7kN4/usnYgk5Ji2L06X7nHBXM9jfNcfCYiLi7pk2rSVcGHNOh+ZTgrUzTv0YzThauq3Rmwd6xqbrv1x2bqrN3bUMalaJuprrGdToD+lc1lvO+2s2uOso5v3+b123vkYo+u26cM5m9OiKaDBk1azTN4ONtPv0pJe9vT5p0YNgnuiKa7EDRrwdOiohWV4nzG6y6E9XOTKbyaXfXZcncvXFcWZkVkjYkXdnaNRrM3lW4jtYnEklHRCW/XNITSdt+ThZASbpPDK9G3zMiTpd0v6gpbhqxH/8aEd8eoX3rK1GSDomIwyuPNyddtZ+xK1qjbr+2gWdusy7pamev8CaiwWQy41ayLZRm33ouqSD35Ii4S2nkjJdHxBFD2o20P5XQ1CQ+vWE7qdvuajk6RG7Tqw24nTTlbqvagDbHTpXPmladge/HpOLIGZ9dNccUvf6N9ZxfSmOeLXEmLTfG535m7welcVbnop3y1+7AB4FX1zWQ9GBJ/yHp68BvSYNnDx01YATHk8azPJuU3D7w01qlf2cXrutm0riPX5f03PyGbuLa3qppENySFrip+kW6TTeUUlHG50m5uJ8D3tewf3OSpJUkPUXSEZKuJR3MNgbG+aHwLuCGfJBfm2a32DeVdKWk1+bH/0U6Qc8Jkt7f96u6q7evyd+/OIbuVB0GoDQsVokbJX1e0jWSTlIavq/OlpIul9QbXuwtQO3MjC2NtP0i4m8RcUN+77+1LrjNvkE6/vX0X0lfygjHwTZKtsVppLs8mwMnSfogaaSXuiBm1P2plEgXFfYhXcwZKgezp5BmmzydNIFQnVNJqRMfJF3p/MrQDo127DweWJ+pfar2nJotFxEfjohXR5pRcFzpQsfR8pw/CZL2kvQRpQLqO0gfNk9jPGlaM2YSEz1AejPPOVGZS1qpivaoYctL+jlppzsKeAPwsWgw7eYI2gzM31M02UD+ZP7ZfFX7eOAwScdExLtqmj6bvtEN6qhvqlnSDEx1U81uQpq1q03uXitaMl/83l8znlvRfyAdJI4iXZ06LSIGpdvMVP9OZclq5TeRxjUc5v4RsbXSKBYfyu2G3h6eBEn3JeUtPlHSxvnXC6gPFELSO4HNJL11iT/M7K3NlSS9FHiApoag6q2nydXHY0n5e7280+NIed3DPJA0OsaFpCBjXdKHmpk00vaTdHa0H3nitmFXNwcoOg62VLItVu39TdKPSEHC6yKi7vjZen8a9XgRBZP4aJrRIUj75DBtawNaHzsrSs6pAKcr1T1sCrybNMvo1xu2bWOjmNzkEG28A3geaTzgn5DOG49karSROWmcAW6v4Es0L/6aqMqJEdIkBQ+pabI36VbILqRAa22lmYcujIg/j6GL1eKKpsUB2wD/KanxQNuQbm+Sxov9GWk6x/NJU37WBbi3kCqCb2LqVta068vbezNgi0p6yAKaBUxjL8qYcM7jxqT9aFfSFZx18lXSCyLiuukazED/SorMfi7pHOAupaKxB5P2kdm2E2kMzE1JxaIinVDrrljuTRq67BmkYrjaq4GFnseSV7zarmfVSmrBYkn/r0GbP5JO+ivn49JDSbdTZ9Ko269x4Fk5RrQtvIHC42BLJdvifpVz4T2kc/B+kurOi633p1GPF1oyhe++wBYNmpUUEbYtMmt97KwoOacCHESqGfklsH9ENJlBrkR/QeCvx7SetiY5W+KMGWcO7sSLv9rSkgnadwJfiIgmVZ+9XLInk95kT4mIfxlD/4qKAwrX9WLSJ+G/Vn63VtRPwHAmsG+TfCSlgqAdgReTrhL3ZoX5UkQMDZpKcvfmE0mPJO1Lu0TELmNaR1GRmaTe1ZjHkfLqto6ISdwCriXprIjYvaDdQRHxkXH0qW89pbNIHUIaS/kKUrB2YTW/dkCblUm5j78mBRnXANtGxDGtO17fv6LtpxYzXc2Hcwi02xaj/k+l+1OJvr7eCZwR9ZP4lMwcWVRkVmnf+NiplpPd9F0tvz/wH8A7SePcznge+CTP+aU05tkSZ9KcG0VhFGqZ/KxU7LTUBoghIwBMgkYo0FPLJHpJq5GuDvyNNIbhkaSE/8Mj4toG6ysZ3aDxVLMqmN2u1CTXVUrSXsCtEXGJ0iQnf4qIsxq2LSky25x0Zb86s860J2JNqIBrmvWuGhG3S3pQ3QelwuffeNDfxnmsUBpYvjdb3Q8bLF+dBal3K3rWi7Fgat8oaNdqFsgJpCXMG6Mcz9pu90q7/gsRd0XNzJG5XaNJFGZCm3VpwHTAMHwCBpsbxllkNhEaLfn5S6Q8msNISd2nUj42bl0/21zxGqVAr20S/amkWbG2Il0t+h5pWxzdcH3bkiZ7uI5U/LVrgzb75w8XKA0rNcyJwN+B3gn+QmBVUsHZTJvkulrLdxy2Jd+mJY37urukRgfafHK6S9KupNzMJnnTp5JSEi6sfA0yqQKue0k6DHibJAGf6M+FnCHvyF+nkt4bhwNnkiaZmFGS1lGalhpSEevTgDcpTRVc5xvAA5i6fT2u9IsSr6lfZEl5v35Ffngk6diykOHvx9br6bCi45nSTFxttzuwdBEh6cPxUJI+Snp/HUbKIx9bCmPbdUXEZ3IgeyKp+O1kUmA84+99m3mTKjIbp1GSn/9CSi8IScuTZlKZ8TEPs9Kih7YFem2T6O8TEe+FlEIQqUIUpWkWmziOtN3PJgVfx5HGAKxr8wPSbY79JD0rIl4xYNn1I+KTvQeRJoe4QtLQKQ8LTXJdJR4UlbEN8750utLEALXywf3+pDzot5AqnetOQLcAX476IhiYXAFX1U4RsW3+eZe8LYaNG9m68Ka3zZUq2J8QEffk48W5M7me7FSmJqI5hfQh/DrgU6T0nmFKirFaGeH/2lbST5gKuqvjfD50QJuNI2InpXFEnwxslY/Vw/I6e+uZrn+D1lNkhG0xqXWVHs82Ktjuo2gzicJsraukQNdmWRcC3FGSn1cBdpP0Q1JQvOq4Okm7oodRCvTazvqzQeX5V6us93417XpKqj4f2gsaIs3XPSzveZRZwtqa5LpKXJ+v4p7Jkv1rWvBQcnD/LrBIqcinV+Az6Lb3pAq4qv4q6XGk12hrakZRiNEKb1YF9sjv4c0ZcrwYYT3LRcRnJO0E/LMXoORbxXVKirFaGeH/+nZB25IZoUrWU2RS6xlhXaXHs9bbXUvOKHrvr0nBYJ1b8wfitXJKwM0N2pQqXde4Z4G0MehaDm6r5GdJDwJeT6rK/Bnw/mgw4cC4aYRCBLWc/WzIupYYRm1I+8+R0hp6AfUWETF0nFSlYqeTmJrt5kURsceQ5YtmCSsxyXW1lYOc/ejrH2kw9aG5cbl949l4Km2WykGryz3ThAq48roeCLyfVFx1PWkWvbGM8iBpU9LxYjPSFMFHzPS6JB1O+rC9OWns0VtJV6SXj5ohCad5L9ceLyZF0oERMXQYxmnatJ4RqmQ9XVZyPCvc7sXnES05icKPSIVVMz6Jwijr0gRmgbSZ16kAtylJW0YeTkSSIm8ESftGRKv8QUmbATdFRG3O7yQS6TWh2c80VcDwc2AjUpDxhry+ocO8SLof6Up7Lyg5PCJ+P9N9tCVN8kQyaZLWJBW0NPp/VDCjkdIsTY9hyRm1aq+QStqFqWKxbzZY/mGk4sHfSVoIPBY4J+bQwbpk+3VV6bZQ4YxQbfenuU6p+PUREXFlTo07seaizJ7Rfga5gZNUNHwPjzTSQ1NaslC01785USgK915kKZ51btLmfZFZoQ9Vfj6/8vOrmjSW9AlJ/y7pHaTk89ppSzW5RPrWs58V6hUwXJevmOxIrrSta5gPSu8mBcRHkm6f2fgF6QrOq0m3sOd9QCLpBZK+D1wKvFhLz2w2SMmMRueT8v13rHzV9e9I0mQofweelx8PFRE/jojf5Z9vjYhvzKXgNiudEaqLGm8LjTgjVMn+NA+cytQYu+uRCrmGKSlm7c1a+nZS/uxupLqdJuNL9wp0f6k0osqfGN/kVdVC0d7XXNJq1rnZ1oUc3FGV7EBbRMSrJB0QEdtJurRBm0kl0pfO1NJWfwHDd4DvNChgQNIbSLfJVyUVOu1KmvJ4mZFvRd8EnDTBT8CnkoqWrmTqRLLvgP4dGREH9xW3DC1qmWTRTcWrgUcD50bExxu+F6HsfXJPRPxHy/49NiJ2yD9/Sg0LAtsoea1mwKSOM/NBm20x6oxQY9+fZsFavbSniHhPTU0GFBSz9tIkJJ0fEfeO9KOGhXMqm6GtxNgLRUc0r3KRl9UAt1rEtV7154bt/yHpQ8BPc4FLk5m4JpVIXzpTS1ujFGQ9IyK2lbQoIk6W1OjKeccsIl2FmeS0t41PJBFxcP7euLhlkkU3FXeQcr9741r/bfji92pbjAlwXv5g8hmmirjqxsG9TWmKz956Zvy2ZslrNQNKtl9XtdkWo84INfb9aRb8Kl/0+A5pMpnf1Sw/SjHrPUrD8F1Hu6uwJTO0lRh7oeiI2s46N6uW1RzcUQurFpLyT84mHWR+VlecNqn8R7WcqWXEdRUVZEk6izQE0qtIVzL+JyKePo4+2pR84LyWqRPJY+qKzOY6SQ8B3sfUtLSHRkTt9LRauhjzmIgY+mFDS858COkK6dBhBSWtDRxaWc9hMZ5pvSeqZPt1Vem2UMGMUF3cn3Ju+wFM1WR8OiLubNCudTFrztU/gHQ19qa8rto8Xi09Q9sbI+KRbdbdsH9ztlAUJpeLPFM6E+BKOiQqU1kqzcC0zhz79HMvSdtFxMU1yzRO6J4miV7k25UNk+iXA/YENgVuiIgz6tqUyon0hzIV7L83Im4Z3mpyJplIP+q6WhY5tj6R9BdyKI1ZPDTnfC4XIOW+7Uu6gr4P6ZZj733ynpbPtUEMqCrPf388af/envQa99Yzlvx7SRtGxK8rjx8VDWYjbLmOGdt+Lde7ANiFdFv/aXW3cSdRrDPpbVG6P83U8azJOatE2yKzSZIk0n73N9LEK88BVge+HmOaKl4TnNWtrfkWZ3WpyGxLSZdLenZ+/BbgdbPZoSqlQeKrDmvQrE1Cd38S/a60SKInXVHdmfRG3l1p+K+xiIjfRcR/RcTuEXHwXApus0km0rdeV0mRY68pcFlEHEi6td/k021/IceBDdrM5QKkU0gfrP5BGgrpivxz7e19Se+S9F1JP5N0A+n/G7Ts0aTZoFYF3gwsIOXsNU7HkXQ/SR+WdKCkT9a34IuS/kfSfSUdAYwj4Czefm1J+ldJBysNzwZpSMHrSMFanUkU60xyW4yyPxUdzwrPWSXaFplN0ueAvUiFbZ8hjQ//N8bz3ppkMXqpOR1nLSUiOvEFXE7KKb4kPz4fOHuG1/EK0tSZK7dosyXwItJ0iS/MX68CzmvQdnH+/o38/ZIGbc7ve3xBw34u7nu8aLZf01ncl1pv90muC7gofz8nf7+04bq+RhpzGFLu3xeHLLsD8DbSMHBvzV/vBb7aYD2L+h4vbtK/ltvteNKMeNWv44Hjatp9q/Lz8ys/125D0pW6BaQpSxf0v9f6lr2w8vPr+l+7Ie3+I79O/0a6APGfpHz9Dzfon4CjSPmdbx/T/lq8/QrWdS3wdFJA9kJS3un2ddtwXPvcLG+Lov2pui2aHmMY4Zw16nbMjxfVLL+IlANb/VrEkHMdcOQ0bYe2yctfUPn5usrPF49pWyyurrf6us+FLyYQZ83kV5eKzP5IOrivLGlPpnLyZtpCYEVSgVAT/fPCC/gD8KwGbUsSukuT6G+XdAhwFSk/8y+Sto8ZvPWg2amyLzHJRPqSdZUUOUK7auUbSQUcezFVyHEHcE2D9UyiAOnthe2uztt7Malg5zWkux1N/i9IxS2rkYKAhUOW+1V+jRbnde5JKlKpKy59OfBE0lCGewHrkK5undagb18g7T9PAN4s6WPRftSHOqNuvzZ+CryEtA3eSkoH+B3pw8W0KqlakyjWmeS2KN2foP0xZpRzVolWRWZRUEwZ5YWYa+bUEJGOu48nffBcpW0fGprkrG4lJhVnzYgu5eCuTMot/DXpjX8NsG1EHDOrHcskvSci3tiyTeuE7hGS6HvJ7cHUgS1iDiW4T8okE+kLX+PWRY65XX+R2aMj4tk1bUoKOeZ0AZKknUlj2C4kVaE3LY7cgJRy8Q/gYFIe3rTpIXkbvKB/PdTMOqc0fOAeTAVypwJ3Aa+OiKHDEkl6YkRcUnn87xHxpbr/q63S7Ve4roWk4Goz0lBaqwBvjYhzBiw/qIB4LMeySW2L0v0pty06npWcs0pMUxtwdMydHNyB6VWRp5uf4fXN6cl45nqc1a9LAW61qKB3VXBoUUFpMUx+Qz6aJQsYmhRyLWTqk9+GEXFZXZtRNS0MkLQiU9tiLDOgaQZnkLP2KieSR5EC3SdFxIxdlZlk0Y2kdSNPhtD3+yfFmIo/Ji2f7N5Ayrs8qu5DTOnxbFkwrgKpUjNV+DVukyx4krQhKWjatclxqc3+ngPVs0lpFn+auV7PvLlcZAazE8eU6lKRWbWogMr3YUqLYc4Hns9UYdeOdQ2UBor+AnA6KYev6YxLrYxQGHAc458B7UOVn1vPIGdlJK0k6SmkgsP9ScHnOsDHG7S9tsWqJlZ0k9cFgKQvVH5fO8zffBERd0TE2yPi9U2u0DO3i/smaoIFUqXm/IxQ4y546h2XJB2RjzPfBzamwXEpa7O/f5mUurNY0qWS3i5pW0lzaqawuV5kNqk4ZqZ0KQe3ZAaQ0tl4SmY02oSGA0WrYJYrSVuSripvKKk3K9gCmucKb9S3LS5s2K7UnDqwQNl2H2FdrfORJb2C9Kl+Ud1tyT5/IN1tOIo0UsYXI2LQrdx+J7RIU1gvIp6Z+3pURJwLnCtprxZ9baq6/6w7huefjyb9Hp5z8i34R1F+HJyUsc8INQPHs3HPvtl/XDqtxXEJWpy/Iw17eQbce6X4DcBFpHzk+7Xv+thMasbTUo3jmLmgSwFuSVFB6axfJTMa3Ul6Ey9Puo271pBlF9F+lqtRCwN+01cY9Oua5UuMOoPcuJVs9yIlhRJZ2yJHSFdFdiEVwFwMrCPptaRK3etq2u4JbJBfqzsYXhA4yaKbNSoFH/et/LzGGNY1X0ziPTxfTKpAqtQkCllHPZ6Nu+BplOMStChmlfRg0rTITyF9ALoaeCUwbS73LJrrRWZt4phZ16Uc3P5PfrVFBVpy1q9eMUxtcvs0iecR9TMaLQA2IB1s9iflAs14rmBpYcAkCoOGFIAQDWaQs5kh6ZGkk8ouEbHLDD/3pIpuJlr8MR9M8x4e2yyGc92kCqRKTbKQtdSkC57aHpfanLMkXUZKZTgX+HY0mBhnNsyDIrOJxDEzpTMB7qRJWgu4P/An4JaI+OeA5RYBvyDdKrqx8v2Xg9pU2raejSd/6lviRa1rU1nXzpV11RbpdVXJdp+k0iLHwnWtQbqd1zuJvH+unYgnZZLbvY2cR9ibbeky0iyBq5EK0345m32bCZJeGhHHVR5vAKwZEdcPaTOxGZdmq2BMDWcxbHs8k7Q+cBDp//nwXCt+m2Qx6yRJemxEXJX3p+cD9yHN6jbr6TXTHGMOIc3oNqePMV0qMpsYpTH7ziYlWe/I8OT255ASxXsVn+eRBs//eYNVlc7GI1Ll9T6kgdGb6CXqV59jWTWJWZAAkHR2QbPWRY4j+CzpSsIhwA358bJqktu9jepsSxeScgpvZ44VqIxgR0mnS+ql9RxOmnRkmEnOuDSxgjGVzWLY9nh2IunD7J9pXvA1SZMsZp0ISZ8lTSQFcCTpSvZCUowxF/QfY9ZhPhxjYg7MNjHKFwUzlJS06Wt/ca999fGAZX9PGsfyAlKQ8FhglYbrWTwD2+fjk1pXV74muS2A9wF7tmxTO3vRDPbvotla91z7avO/UzDr4Qj9muhsS7Ow3S8n3S3rzeD3TeCsBm0mMuMSLWYKm4FzT+tZDNsez1hy1rRWbSe0P0xsBrkJ/k+L8vcHkyZp6t1db7RfTKB/8/IYM++LzKJghpKSNn1uyxW6K0vagfRJd5CHkILaHUgnvLcDv5P0swbrb104p6mZfADuy9Qc33XLT2Lmn/likttiG+A/JX0vryuifla3kiLHUtdK+hRThZjXjmk9c5akjfOPbbd764LAktE1mPxsS5N2J2nq2NVyvupmpLsJw0xyxqXGBWMzcO4pmcWw7fFsYaUAeN38c6//c+GK3SSLWSflT5JeB+wNvAtYIGmfWe5T1bw8xjgHt0DOaTqUqUTww2OaQefzsteTruL+L0vm4N4UEUMPuIWFc29j6gR5F3BGRPygxToar6urSrb7JJUUOY64vj3IhZgRcda41jNXDSloG+t2b6rrBXf5ePsEUlrXnqTpxLeJIYWpmuCMS5MsGFPBLIZtj2fz4ZwwqWLWSZG0Kind4tcR8fU86sPLSDnQv53d3s3fY0ynAlxJjwY2BW6IiO8NWW5gXmqTK3WSDiINNVKd0nYsJzo1nNUkJ6Y/izQF631Jb/rvkMY8rStCmLPJ7bOl6XafLU2LHGdgPcuRgopNSe+rM8axnvlI0gMjokkufW/5RkVBtqS+4r7e1ey6O1mtZ7acpEkXss7l49lsFelZ93UmwJX0EdLMH9eRgs/rI+K/Byzb+4S6Ayk5/crcZvWIeFKDdV0GPBe4N6gYxy1ipVlN7k+6JfcW4BUR8W8Dlj0eWJk0nuDtpMHNnwTcPuwTlqTPAHdGxAH51tdCUoHBNhGx9wz+O/NGm+0+G3KR496kQsLDgd0i4oXDWxWv61TgFuB7pCBjjYh47vBWkyPp2IjYf0LrOjGmBpZH0qUR8YSaNp8g5YxuSRql5ObIk2FYM5IuJqXG3Jp/1eRO1tWkApheGyLiM+PqY1uT7N88OJ6dBhwNPANYmzRhzFNmt1fWBfM+B7fiMRGxXe+BpIFjs/VubUk6Pyrj7UlqOivHLaST1k3kqwOkT+Mzrc2sJg+eJjj/WD45DLNxROyUb4k8GdgqIqLFtuiisc8mI+nIiDi4L+eyLtey5xkRsa2kRRHxOUmvnun+VawblXnhc3/nEknaJiKuGOMKNiYFB1tU7v4soFn+4xYR8SpJB0TEdpIuHVc/O6xk5siSmS1b0WgzhbXqn8pnMYS5PzvW2Gd1s2VTlwLcW/KQMFeRbtP/StLGNVdW78npBteRcgybWpF00Bj3AMxtZjW5NR9wLyIVEqxGylGaNje4Yq4nt8+Gsc8mM2KxSZsix1HdLukQpt5Xf5G0/RwqQFwJ+Kakc5gq0pvpdKHNSO+ltfJ3kWZ1a7KeVkVBhUVmXVdSVDmJQtFRZgor6V/JLIYw92fHmsSsbrYM6lKKwvEseWLonRQGnoQkrUmaNWQz0ifxT0fEHxqs6ypSOsAtvd+N4+SjpWc1+XRE3DFg2QXAfwL/ShqA+TZS5fvHIuJvQ9Yxp5PbZ8M0232uzSbTuMhxBtY1XcHJXCo22aT/d3VFNyOs67i2wXNJUZAtqaSoch4Uivb6F0ydq8bSvzbnkdkwySI9W7Z0KcB9DbAVkyn8WpF09ebhwPeBz8QMTmsr6bj+X+Xv0/5P+WreBRHxq5nqg42Xlp6NZ83e36JmNp5JFTnOl+LDSsHdH0kFd7NexKWpocWWMo58fVvapAqrSgrGJK3X16ZREZxazKbX9jwyW1xkNmWubgtJe0bE6ZLu1+Qi4FzRpQC3deGXpLMj4mkF6zoR+AlplIJtSfmvLxjeqtXzn0IqSrmbVFxxdf66abr/KQfcLybNYHRsRNzav4w1V7pftFzHaaQPRx8DziCNj7wV8NSIeGpN27EXOSrNrPP3uV582Fdw915g13EV3LVRuer4cNIdle8CjyQVdG7doP3ywGOYGmdyw4iYK7MazXmTLKwqKRibpk3TALdxwV3b88hscZHZlLm6LXL+9pN732e7P011KgeX9oVf3+t9Mmm5ro0qAe05ki5s2X6oiHg2gKS1gf1IwesHSQOVP3ya5e8GPp3TDfaXtBLptrpv85Qp3S/aWC9yNb2koyLiHNK+tFeDtpMoctxonhQfVgvuTpb0qpleQUlebOSRSySdBzwhIu7JQeu5DVd7GikXcTPgN6T832UqwJX0+oh4X1/6WW3qWTbJwqqSgrbSIrjGBXdtzyOzyEVmU+bqtghJ7wQ2y/ncU3+YQ6k//boU4JYUfpXMIgXwG0mHMjW7069b93YISV8gffK+i3Tl5/PA60kTRAyU//ePSloDeJmku0hXdOdM/ug8UbpftDHKbDyTKHKcL8WHYy+4KywE7FkV2CPvS5vnx02sATwTODUi9tOQUWE6rHcV9O0FbSdZWFVSMFZaBNe44K70PDILXGQ2Za5ui71JdxifQTpnaejSc0SXUhQmUviV17US8HLy7E6kq6V3zeDzz8jMSbnAZX9S/34/I52zGaPC2Xgmsa/Pl+LDSRbclZC0KSmo2IwUWBwRET9r0O5k4OukE8r3gGdHxFbj62m3TLJQtKSgrbQIrk3B3UydR8bNRWZTptkWv4iI22a7Xz2SDoqIj8x2P5rqTIAL957sqjlrtWNO5iCw2uayIcsuAn5BZbrd/P2XMaaZpGxyNOIMd5My7iLH+WRSBXcD1r3BuIL9XGxyP1L+5D6kDz9Dp/ZeVqjlDHKTUFLQVloEpwnNYjhJkyoInC8kbUiaYnrXqIxDbu10JsCVdCxpOtG1SRWIEZWJH4a02YyU31bbRtL6wCNIM6BtT5offXnSG3Kp4YpmwyQKpLpKI85wNynjLnKcTyZRcFdZ17tJV1RXy7/6W0RsOaZ1Ka/rQaQP0afHHBgdYjaobAa5iR0HSwraSovgNMFZDCdlkgWBc1W+K7w9Kah9CrAJ8BHSxB6LZ7Fr89pys92BGbQJ8DTgBlKA0uRksAlph2ra5vvAWcATSeNabgssmCvBbfY9SXvOdifmo4h4R6RZ7iIidomIN0bE7pQN5D5OG0XEuyLinNzfgUNSLQN6BXcnkPISTxjjup5I+lD7HVJu48DRSnpFTZKOl3Rc/jpeSw/dNMjJwO6kPMudgVNG6vk8JGnjnFe9haTt89fTaPZ+nORx8JER8e/AnyPiTFL+9DjaQC6qBP4QEZ8jTU8/35Vuiy75A3AmKSbbGbg2It42V4JbSYskXdD3tWgOFh0voUtFZneSdozlSeOLrjWGNg8BHksKhp9GKn74naSfjViIMpMmUSDVdaUz3E3KWIsc55lJzSrYsxXpCu6WpNzpQUYpkAK4f0Q8t/dA0uLC55nPRplBbpLHwZKCttIiuEnOYjgpc32mtUnYGNiFVGh8MbCOpNeSxre/bjY7BiMX2s6aLqUoLAA2IH263x84LyKGVh63bSPpeuD3pDyhG6nk4jo/rjtUOMPdpIy7yHE+kXQlKYd+EsWlG5DyBP8BHAx8PSJOneF19PLA/xu4gvQh5rHAkyJij5lc13yhghnkJmmagrbamcJKi+DmelFliZLt13WSHkkKdneJiF1muz/z1bwPcHO19wHATyONd/g6Uj7t8YPeJCVt5gulGbJ6BUg/AE6IiH/Mbq9sJrjIcWn5is8SomaQ/RHXVy3wuXlQXqykLSLiB32/Wx54WUR8asjzTzc1MpBSaMp6Pb/lyvJH0WD2riHPsV1EXDzD/Wo9U5hGHF97NosqxyUH7TvTclY3m7z5Fl90IQf3RODvwA/z4wtJCfjDBkUvaTNfHA+sD3yDdLVp0FAxNv88hzT7Ue8gcx7w8/y1rFuFVHwztmLAXOBzNuk4sSPD831PkvSSStvnk3L4Hz1sHb088BzMfgm4HDgV+MBInZ/fvkmaKnqn/LVjXQOlCTaqDpv5brEqKUXosaQ0t2uB4xielvKaEdf5HNK41G/LX8PWNV/0zlU982KM1WXUvIovunAF95KIeGLT35e2mS+UZnTaqfJ4cUTsOItdmjdUMGPVJEn6PXBfUo7WuaQA94fz/a7DTJL08Yh49Zie++KI2K73Hus9HrDsAuATpCDowaSB9t/RdHgrV5ZPkXRRRAwcwq9v2S1JHyLeQBplAGABsE/UTIE9Qv+qM4VtA/w4IqadKUzS7cCv+n9NOsY8tMG6vkqa8vneWQznwrFpFD5HzR/zLb7oQpHZ+bmS7yzgj6QCkKeShniayTbzxW8rBUiPJ03zaQ3Mg0T6+VDkOFFacuzi1YEtxri6NgU+WwGfIgU9C0ijIqwvaf1oMD43k51qdq5rPHsXU1f/qt//AMz4WKIqmyns2yO+VyddVDkJpbO62eTNq/hi3l/BBZD0eFJC9nrAbTSYEaqkzXxQKUDq5cgcu6wWIHWNixyX1pezeidwRn/u6wyuq3GBj9IsUsHSt1sb5UxK+iJpBrO9gQ8Bu0fEfuW9n780NSNXb3vWbkNJ74mIN06oX/2G5eAeGBFHjbDOic3YOSnT5J1HNJjVzSZvmvjimJjDkwx1IsC1KTkJfH+mZrma00ngZqPIhVurkYpEtwOujIgZnb89X7Vd4lf5+9iKYUqr7LsoXy2/N7iF4Vf4JD02Iq6SJFLu7srAiRHx90n0d5y0DMxiOI6CQBtNjiv2BRaRZlZci6n34ntmsWtDdaHIzJZ0PLAuqRhmzieBm43oC8C/AkcCLwO+MoZ1bFT5ehvpffWA/Hhc7iS9d19DupK7/BjXNdftSCouexrwQWBgjrWkzwKvyA8/mNsspBsFxJCK2KrH96YTh8xZEyoItNGcQvqw/Q/gBaQhDP9Bel/OWV3IwbUlPSCmprU8ZxkdIL6IpCMj4uC+YrNOFHJ02DoRca6kgyNiN0mXzPQKIuL/9X6W9JQJXbE4FTiaNF3v2sCbSFN4LnOqw6NJehMw7Bb/RrkA8MHAk4GtIiI0x2dcamGjvuP7hbPamxFUCgI3rNwlWUAa4cjmlvUi4pkAko6KiHOBcyXtNbvdGs4BbvdUk8C3ZY4ngc8lEXFw/j6nP5XaEv4vV5ZfJWl3YEbTEwAkPaHy8L7Vxw0LxkqMPXCfLyRVp6JenVRsOcif8rjme5OG01ogaZ9x9m/CujSL4cQKAm1kV+fj7GJSse1rSDVM18xmp+o4B7dj+ma5+j4uMrMOy5MAbB4RV0vaCrgxIv4yw+toXUw0A+s8A/gnqZDjEuCgiNhtHOua6yoFe5BGLDglIhYPWHZV0i3UX0fE1/OV3JcBH46I306iv+OkDs5iOImCQBudpJ1J6UILgb8wDwrzHeB2RN9wSdCwIMOWlqvln8ySMyd5Zp05qKuv1SQC97muctu6dywbe3HfXOVZDM3ac4pCd/Ruq+8A3A1cRZrScXXGOLtTR32DNGPYrbPdEavVydcqIv4u6Zf59vyfSFfsLpvlbk1atYjvpcCxVD64L2OeAzyCdHx/KfAEUuHhL4FNZrFfZnOWr+B2jKTzI2LnyuMLXCDVzlyfncWmdPW1knQsaRaztUhDoMWgWdOWBf0zKC1rujiLoYt6bdx8Bbd77pF0EHAd453VqXMqaR6eWWf+6OprtQmwG2kGtOcAXRkFoLFZKu6bqzo3i6GLem3cHOB2z76kAeKfRcrTeubsdmde2Yl0JeFu0uD62zB1S7QLQVMXdfW1uhPYmXQbel/SldxlzcsrP19XeRzAshbgXsrULIYXkz743Eg6xpvZNJyi0DG56GZnpopulrmCjFKSFpA+HPwkIs7Mww3dDhw/n28Fdtk0Mzt1Yua+vC9uQArg9wfOi4hvzW6vzGaOpFdHxMdnux/WXZ7JrHu+QZrhpkeDFrSlfJYU0P4gP74QWJXuzILURf0zO83rmfuU7EoaAP8XpKlmVyJdrTPrknvvLkr62Gx2xLrJKQrdc1tEHDHbnZin1o+IT/UeRMQVwBWS9p7FPtlwnZnZKfsc8GdgHeBw0pSYf8q/92go1lWbz3YHrHsc4HZPV4tuJuH8PKXnWcAfgdWApwJXzmqvbJjqzE6PZ37P7ARpSsznAEi6LiL+K/+8y+x2y2zGrS/puaS7jL2fAYiIz81et6wrHOB2T6/o5nH5cVeKbsYuIt4q6fGkKQgfBNwGfHquz9ayjHsxqfhoH1JqyYtnszMzYM28Dwr4R/55OWCV2e2W2Yz7AlPTLld/dmGQzQgXmZnZvJOnZD0A+GmXCgKHTAtMRLxkkn0xM5vPHOCa2bwj6Uukwe7PiYhfSNqGNEbodhGx16x2zszMZp0D3I6Q9FOWvrXTmxXmobPQJbOxkXRJRDyx6e/NzGzZ4hzcjoiIh9QvZdYZLgg0M7OBfAXXzOalSkHgeqSCwEtcEGhmZuAA18zMzMw6xjOZmZmZmVmnOMA1MzMzs05xgGtmBkh6l6RLJX1F0mpDlnuUpEdNsGuD+nGCpMPyz2+X9PZZ7pKZ2ZzhANfMlnmSngA8CXgicC5pEolBHpW/5oKXSVp5tjthZjbXeJgwM7M0GsNZERGSzgG2yFdxTwVWBm6KiJfkK6Z7A0h6QUTsnGdV+yywLvC9iDhQ0irAl4H7AT8Dvgd8ADgBuD/wK+AlEXGXpMXAFcCWEbGrpHcAP4qIz0t6G/DjiDhlQL+/Dzyv92BAn68CfgfcBawPfD5/nQCsAZwREYeNugHNzOYSX8E1M0tDjf0RICJ+HhFnABsARwFPAzaVtF5EHAocDhweETvntgcA34+I7YENJG0J/AspiH0i8KCIeA/w8rzcDsBPgJfm9tsCl0XErvnxZ4Hn5J93A746pN9HAa+oPF6qz8CqwL7AlsBzga2AQ4EvRMQTgL0k3a/xljIzmwcc4JqZpXF0VwOQ9DhJ/wPcDbwMOBlYG1hlQNuHAXvnK7EPBDYEfg08FrgI+HBebnPg2/nnbwMPzz9/PyK+3HuyiPgZsLqkHfPf/j6k3zcDPwJ2zI+n6/MtEfFX4Cbgn6QZDh8GvCr3eQHpqrKZWWc4wDUzg0tIM6EB7ADcAewPnEa6mvq3yrJ3kK6KIknAj4EPRcSOwJuB/yVdeX1XRDw+Ik7O7X5AulpL/v6D/PNfp+nPKcBxpKu5dT6Y+8yQPvf7MXBI7vPh5KvXZmZd4QDXzAy+Bvxc0qWkYrPjgfNIt/IvyMtsmL+fB+wj6ZK87KeBp0m6CHgl8EvgGuCjki6QdIqkRwDHkHJ7LwIeQsqBHeQ0IICL6zoeEdcAF1b6Nl2f+x0OvC7/D7sBt9Stx8xsPvFMZmZmM0zSy0lXUe/OX0dExOKGbbcgBdifiohjx9ZJM7MOc4BrZmZmZp3iFAUzMzMz6xQHuGZmZmbWKQ5wzczMzKxTHOCamZmZWac4wDUzMzOzTnGAa2ZmZmad8v8BlBzeGu3Z8k0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "category['Sales per customer'].sum().sort_values(ascending=False).plot.bar(figsize=(12,6),title=\"按照商品类别销售额的分布情况\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'按照商品类别销售额的分布情况'}, xlabel='Category Name'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "category['Sales per customer'].mean().sort_values(ascending=False).plot.bar(figsize=(12,6),title=\"按照商品类别销售额的分布情况\")\n",
    "#也可以看看平均销售额的分布"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4按照不同的时间维度对销售额进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-31 22:56:00', '2018-01-13 12:27:00',\n",
       "               '2018-01-13 12:06:00', '2018-01-13 11:45:00',\n",
       "               '2018-01-13 11:24:00', '2018-01-13 11:03:00',\n",
       "               '2018-01-13 10:42:00', '2018-01-13 10:21:00',\n",
       "               '2018-01-13 10:00:00', '2018-01-13 09:39:00',\n",
       "               ...\n",
       "               '2016-01-16 06:49:00', '2016-01-16 06:49:00',\n",
       "               '2016-01-16 06:28:00', '2016-01-16 06:07:00',\n",
       "               '2016-01-16 05:04:00', '2016-01-16 03:40:00',\n",
       "               '2016-01-16 01:34:00', '2016-01-15 21:00:00',\n",
       "               '2016-01-15 20:18:00', '2016-01-15 18:54:00'],\n",
       "              dtype='datetime64[ns]', name='order date (DateOrders)', length=180519, freq=None)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按照不同的时间维度（年 月 星期 小时）的趋势来进行数据的探索分析  就是看看每年、每月、每季度、每天的销售额情况 包括均值 、总量等等\n",
    "# order date (DateOrders)  转换成时间戳\n",
    "temp=pd.DatetimeIndex(data['order date (DateOrders)'])\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_weekday</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>314.640015</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>HollowayCally</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LunaIrene</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MaldonadoGillian</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TateTana</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>134.210007</td>\n",
       "      <td>298.250000</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 11:24</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>HendricksOrli</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180514</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 3:40</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PetersonMaria</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>ClarkRonald</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180516</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>141.110001</td>\n",
       "      <td>391.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bristol</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 21:00</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SmithJohn</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180517</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>186.229996</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 20:18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SmithMary</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180518</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>168.949997</td>\n",
       "      <td>383.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 18:54</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>OrtegaAndrea</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>180519 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0          DEBIT                         3                              4   \n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "3          DEBIT                         3                              4   \n",
       "4        PAYMENT                         2                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180514      CASH                         4                              4   \n",
       "180515     DEBIT                         3                              2   \n",
       "180516  TRANSFER                         5                              4   \n",
       "180517   PAYMENT                         3                              4   \n",
       "180518   PAYMENT                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "4              134.210007          298.250000  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180514          40.000000          399.980011  Shipping on time   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "180516         141.110001          391.980011     Late delivery   \n",
       "180517         186.229996          387.980011  Advance shipping   \n",
       "180518         168.949997          383.980011  Shipping on time   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "4                        0           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180514                   0           45         Fishing      Brooklyn  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "180516                   1           45         Fishing       Bristol  ...   \n",
       "180517                   0           45         Fishing        Caguas  ...   \n",
       "180518                   0           45         Fishing        Caguas  ...   \n",
       "\n",
       "                                     Product Name Product Price  \\\n",
       "0                                    Smart watch     327.750000   \n",
       "1                                    Smart watch     327.750000   \n",
       "2                                    Smart watch     327.750000   \n",
       "3                                    Smart watch     327.750000   \n",
       "4                                    Smart watch     327.750000   \n",
       "...                                           ...           ...   \n",
       "180514  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180516  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180517  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180518  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status  shipping date (DateOrders)   Shipping Mode  \\\n",
       "0                   0              2/3/2018 22:56  Standard Class   \n",
       "1                   0             1/18/2018 12:27  Standard Class   \n",
       "2                   0             1/17/2018 12:06  Standard Class   \n",
       "3                   0             1/16/2018 11:45  Standard Class   \n",
       "4                   0             1/15/2018 11:24  Standard Class   \n",
       "...               ...                         ...             ...   \n",
       "180514              0              1/20/2016 3:40  Standard Class   \n",
       "180515              0              1/19/2016 1:34    Second Class   \n",
       "180516              0             1/20/2016 21:00  Standard Class   \n",
       "180517              0             1/18/2016 20:18  Standard Class   \n",
       "180518              0             1/19/2016 18:54  Standard Class   \n",
       "\n",
       "       Customer Full Name order_year order_month order_weekday  order_hour  \n",
       "0           HollowayCally       2018           1             2          22  \n",
       "1               LunaIrene       2018           1             5          12  \n",
       "2        MaldonadoGillian       2018           1             5          12  \n",
       "3                TateTana       2018           1             5          11  \n",
       "4           HendricksOrli       2018           1             5          11  \n",
       "...                   ...        ...         ...           ...         ...  \n",
       "180514      PetersonMaria       2016           1             5           3  \n",
       "180515        ClarkRonald       2016           1             5           1  \n",
       "180516          SmithJohn       2016           1             4          21  \n",
       "180517          SmithMary       2016           1             4          20  \n",
       "180518       OrtegaAndrea       2016           1             4          18  \n",
       "\n",
       "[180519 rows x 58 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提取里面的 year month day hour\n",
    "data['order_year']=temp.year\n",
    "data['order_month']=temp.month\n",
    "data['order_weekday']=temp.weekday\n",
    "data['order_hour']=temp.hour\n",
    "data#data里面就添加出了几个字段 order_year\torder_month\torder_weekday\torder_hour都是按照对应的时间来给出对应的 月份、星期、小时"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'每小时的平均销售额'}, xlabel='order_hour'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对销售额度进行探索  按照时间的维度\n",
    "plt.figure(figsize=(12,10))\n",
    "plt.subplot(2,2,1)\n",
    "df_year=data.groupby('order_year')\n",
    "df_year['Sales'].mean().plot(title=\"每年的销售额平均值\")\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "df_month=data.groupby('order_month')\n",
    "df_month['Sales'].mean().plot(title=\"每月的销售额平均值\")\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "df_weekday=data.groupby('order_weekday')#先按照order_year进行聚合\n",
    "df_weekday['Sales'].mean().plot(title='每周的平均销售额')\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "df_hour=data.groupby('order_hour')#先按照order_year进行聚合\n",
    "df_hour['Sales'].mean().plot(title='每小时的平均销售额')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.5用户RFM分层管理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9/9/2017 9:50'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#下面开始做RFM模型  \n",
    "#先从时间的维度来看看\n",
    "#order date (DateOrders) 这个字段是订单的下单日期    通过这个字段来做其他的 \n",
    "data['order date (DateOrders)'].max()#找出来了 最后一笔订单的时间  是2017.9.9的9:50\n",
    "#这样做 可能会有错误   因为没有转成时间的类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9/10/2016 23:18     5\n",
       "12/19/2015 8:58     5\n",
       "1/29/2017 17:19     5\n",
       "5/11/2015 4:43      5\n",
       "8/18/2016 5:38      5\n",
       "                   ..\n",
       "7/15/2016 7:05      1\n",
       "12/19/2017 21:46    1\n",
       "10/20/2017 11:42    1\n",
       "11/14/2015 21:27    1\n",
       "10/15/2015 13:05    1\n",
       "Name: order date (DateOrders), Length: 65752, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['order date (DateOrders)'].value_counts()#肉眼可见的   12/19/2017 比9/9/2017要大   没有转成标准的时间内格式 出现了把9当成最大的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017-07-22 05:31:00    5\n",
       "2017-04-29 06:20:00    5\n",
       "2015-12-30 04:34:00    5\n",
       "2015-06-27 17:26:00    5\n",
       "2015-07-11 22:19:00    5\n",
       "                      ..\n",
       "2017-11-06 01:20:00    1\n",
       "2017-09-07 19:17:00    1\n",
       "2018-01-29 00:10:00    1\n",
       "2018-01-07 08:58:00    1\n",
       "2016-05-21 19:36:00    1\n",
       "Name: order date (DateOrders), Length: 65752, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#所以要把时间类型进行一下转换\n",
    "data['order date (DateOrders)']=pd.to_datetime(data['order date (DateOrders)'])\n",
    "#转换完之后再看一下时间\n",
    "data['order date (DateOrders)'].value_counts()#这样就编程了标准的时间的格式了   \n",
    "#肉眼可见的发现了2018年的数据  刚才那个9放前面 所以导致出现把9/9/2017当成最大值的情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-01-31 23:38:00')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#时间转化完了  然后再看看时间的最大值\n",
    "data['order date (DateOrders)'].max()#这样就找出了时间的最大值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2018, 2, 1, 0, 0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们不妨假设我们现在的时间是 2018-02-01的时候  以这个时间点来  做RFM模型中的 R：Recency，最近一次消费时间间隔\n",
    "import datetime\n",
    "present=datetime.datetime(2018,2,1)\n",
    "present#present这个变量就保存了 这个时间点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30397    5\n",
       "16203    5\n",
       "42806    5\n",
       "49302    5\n",
       "60278    5\n",
       "        ..\n",
       "41140    1\n",
       "34752    1\n",
       "75636    1\n",
       "73587    1\n",
       "2049     1\n",
       "Name: Order Id, Length: 65752, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Order Id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order date (DateOrders)</th>\n",
       "      <th>Order Id</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>499.950012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>1819.730034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>18</td>\n",
       "      <td>3537.680094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>380</td>\n",
       "      <td>14</td>\n",
       "      <td>1719.630030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>457</td>\n",
       "      <td>7</td>\n",
       "      <td>1274.750023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20753</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20754</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20755</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>327.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20756</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20757</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             order date (DateOrders)  Order Id        Sales\n",
       "Customer Id                                                \n",
       "1                                792         1   499.950012\n",
       "2                                136        10  1819.730034\n",
       "3                                229        18  3537.680094\n",
       "4                                380        14  1719.630030\n",
       "5                                457         7  1274.750023\n",
       "...                              ...       ...          ...\n",
       "20753                              0         1   215.820007\n",
       "20754                              0         1   215.820007\n",
       "20755                              0         1   327.750000\n",
       "20756                              0         1    11.540000\n",
       "20757                              0         1    39.750000\n",
       "\n",
       "[20652 rows x 3 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算每个用户的RMF指标\n",
    "#按照Order Customer Id即订单客户的ID来进行聚合\n",
    "#计算每个用户的RFM的指标\n",
    "customer_seg=data.groupby('Customer Id').agg({\n",
    "    'order date (DateOrders)':lambda x:(present-x.max()).days,#这个地方用现在-用户最近一次订单时间 就是R值 最近一次消费时间间隔\n",
    "    'Order Id':lambda x:len(x),#这里求len() 是订单的数量 根据用户的ID来求每个ID里面订单ID数据结构的len  就代表F消费频率  这个用户的消费频率\n",
    "    'Sales':lambda x:x.sum() })#根据用户的ID 来求每个ID的Sales的总和  那就是 M 消费金额\n",
    "customer_seg  #R最近一次消费时间间隔  F消费频率  M消费金额  就是下面准备的这三个字段 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_Value</th>\n",
       "      <th>F_Value</th>\n",
       "      <th>M_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>499.950012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>1819.730034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>18</td>\n",
       "      <td>3537.680094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>380</td>\n",
       "      <td>14</td>\n",
       "      <td>1719.630030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>457</td>\n",
       "      <td>7</td>\n",
       "      <td>1274.750023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20753</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20754</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20755</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>327.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20756</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20757</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20652 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R_Value  F_Value      M_Value\n",
       "Customer Id                               \n",
       "1                792        1   499.950012\n",
       "2                136       10  1819.730034\n",
       "3                229       18  3537.680094\n",
       "4                380       14  1719.630030\n",
       "5                457        7  1274.750023\n",
       "...              ...      ...          ...\n",
       "20753              0        1   215.820007\n",
       "20754              0        1   215.820007\n",
       "20755              0        1   327.750000\n",
       "20756              0        1    11.540000\n",
       "20757              0        1    39.750000\n",
       "\n",
       "[20652 rows x 3 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将customer_seg的字段改名 \n",
    "customer_seg.rename(columns={'order date (DateOrders)':'R_Value','Order Id':'F_Value','Sales':'M_Value'},inplace=True)\n",
    "customer_seg#早都想改了  忍了好久了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_Value</th>\n",
       "      <th>F_Value</th>\n",
       "      <th>M_Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>293.040008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>159.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1499.825033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>307.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2915.880065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      R_Value  F_Value      M_Value\n",
       "0.25     75.0      1.0   293.040008\n",
       "0.50    159.0      7.0  1499.825033\n",
       "0.75    307.0     15.0  2915.880065"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#将RFM划分为4个尺度\n",
    "quantiles = customer_seg.quantile(q=[0.25, 0.5, 0.75])#quantile四分位数 第一分位数 第二分位数（中位数）  第三分位数 \n",
    "quantiles#这样就把第一 第二 第三分位数给求出来了 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'R_Value': {0.25: 75.0, 0.5: 159.0, 0.75: 307.0},\n",
       " 'F_Value': {0.25: 1.0, 0.5: 7.0, 0.75: 15.0},\n",
       " 'M_Value': {0.25: 293.0400085, 0.5: 1499.82503324, 0.75: 2915.8800654175}}"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把它转成一个dict格式 方便后续的索引\n",
    "quantiles=quantiles.to_dict()\n",
    "quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#写一个函数 转化为RFM的值  R是最近一次消费间隔 越小越好    F是消费频率 越大越好   M代表消费的金额（一段时间内的） 越大越好\n",
    "def R_Score(a,b,c):\n",
    "    '''R是最近一次消费间隔 越小越好'''\n",
    "    if a<=c[b][0.25]:\n",
    "        return 4\n",
    "    if a<=c[b][0.5]:\n",
    "        return 3\n",
    "    if a<=c[b][0.75]:\n",
    "        return 2\n",
    "    return 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def FM_Score(a,b,c):\n",
    "    \"F和M的值越大越好\"\n",
    "    if a<=c[b][0.25]:\n",
    "        return 1\n",
    "    if a<=c[b][0.5]:\n",
    "        return 2\n",
    "    if a<=c[b][0.75]:\n",
    "        return 3\n",
    "    return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R_Value</th>\n",
       "      <th>F_Value</th>\n",
       "      <th>M_Value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>499.950012</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>1819.730034</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>18</td>\n",
       "      <td>3537.680094</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>380</td>\n",
       "      <td>14</td>\n",
       "      <td>1719.630030</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>457</td>\n",
       "      <td>7</td>\n",
       "      <td>1274.750023</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20753</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20754</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20755</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20756</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20757</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20652 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R_Value  F_Value      M_Value  R_Score  F_Score  M_Score\n",
       "Customer Id                                                          \n",
       "1                792        1   499.950012        1        1        2\n",
       "2                136       10  1819.730034        3        3        3\n",
       "3                229       18  3537.680094        2        4        4\n",
       "4                380       14  1719.630030        1        3        3\n",
       "5                457        7  1274.750023        1        2        2\n",
       "...              ...      ...          ...      ...      ...      ...\n",
       "20753              0        1   215.820007        4        1        1\n",
       "20754              0        1   215.820007        4        1        1\n",
       "20755              0        1   327.750000        4        1        2\n",
       "20756              0        1    11.540000        4        1        1\n",
       "20757              0        1    39.750000        4        1        1\n",
       "\n",
       "[20652 rows x 6 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#新建R_Score,用于将R_Score=>[1,4] 同理\n",
    "customer_seg['R_Score']=customer_seg['R_Value'].apply(R_Score,args=('R_Value',quantiles))#这样就把R_Score那一列的数字完成了分数的转变\n",
    "customer_seg['F_Score']=customer_seg['F_Value'].apply(FM_Score,args=('F_Value',quantiles))\n",
    "customer_seg['M_Score']=customer_seg['M_Value'].apply(FM_Score,args=('M_Value',quantiles))\n",
    "customer_seg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#接下来计算用户分层\n",
    "def RFM_User(df):\n",
    "    if df['R_Score']>2 and df['F_Score']>2 and df['M_Score']>2:\n",
    "        return \"重要价值用户\"\n",
    "    if df['R_Score']>2 and df['F_Score']<=2 and df['M_Score']>2:\n",
    "        return \"重要发展用户\"    \n",
    "    if df['R_Score']<=2 and df['F_Score']>2 and df['M_Score']>2:\n",
    "        return \"重要保持用户\"\n",
    "    if df['R_Score']<=2 and df['F_Score']<=2 and df['M_Score']>2:\n",
    "        return \"重要挽留用户\"    \n",
    "    \n",
    "    if df['R_Score']>2 and df['F_Score']>2 and df['M_Score']<=2:\n",
    "        return \"一般价值用户\"\n",
    "    if df['R_Score']>2 and df['F_Score']<=2 and df['M_Score']<=2:\n",
    "        return \"一般发展用户\"    \n",
    "    if df['R_Score']<=2 and df['F_Score']>2 and df['M_Score']<=2:\n",
    "        return \"一般保持用户\"\n",
    "    if df['R_Score']<=2 and df['F_Score']<=2 and df['M_Score']<=2:\n",
    "        return \"一般挽留用户\"   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>R_Value</th>\n",
       "      <th>F_Value</th>\n",
       "      <th>M_Value</th>\n",
       "      <th>R_Score</th>\n",
       "      <th>F_Score</th>\n",
       "      <th>M_Score</th>\n",
       "      <th>Customer_Segmentation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Customer Id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>792</td>\n",
       "      <td>1</td>\n",
       "      <td>499.950012</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136</td>\n",
       "      <td>10</td>\n",
       "      <td>1819.730034</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>重要价值用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>229</td>\n",
       "      <td>18</td>\n",
       "      <td>3537.680094</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>380</td>\n",
       "      <td>14</td>\n",
       "      <td>1719.630030</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>重要保持用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>457</td>\n",
       "      <td>7</td>\n",
       "      <td>1274.750023</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>一般挽留用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20753</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20754</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>215.820007</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20755</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20756</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11.540000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20757</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>39.750000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>一般发展用户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20652 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             R_Value  F_Value      M_Value  R_Score  F_Score  M_Score  \\\n",
       "Customer Id                                                             \n",
       "1                792        1   499.950012        1        1        2   \n",
       "2                136       10  1819.730034        3        3        3   \n",
       "3                229       18  3537.680094        2        4        4   \n",
       "4                380       14  1719.630030        1        3        3   \n",
       "5                457        7  1274.750023        1        2        2   \n",
       "...              ...      ...          ...      ...      ...      ...   \n",
       "20753              0        1   215.820007        4        1        1   \n",
       "20754              0        1   215.820007        4        1        1   \n",
       "20755              0        1   327.750000        4        1        2   \n",
       "20756              0        1    11.540000        4        1        1   \n",
       "20757              0        1    39.750000        4        1        1   \n",
       "\n",
       "            Customer_Segmentation  \n",
       "Customer Id                        \n",
       "1                          一般挽留用户  \n",
       "2                          重要价值用户  \n",
       "3                          重要保持用户  \n",
       "4                          重要保持用户  \n",
       "5                          一般挽留用户  \n",
       "...                           ...  \n",
       "20753                      一般发展用户  \n",
       "20754                      一般发展用户  \n",
       "20755                      一般发展用户  \n",
       "20756                      一般发展用户  \n",
       "20757                      一般发展用户  \n",
       "\n",
       "[20652 rows x 7 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer_seg['Customer_Segmentation']=customer_seg.apply(RFM_User,axis=1)\n",
    "customer_seg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6不同地区的支付情况分析与可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DEBIT       69295\n",
       "TRANSFER    49883\n",
       "PAYMENT     41725\n",
       "CASH        19616\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下一个阶段的任务  显示不同地区的支付情况  是现金 还是信用卡 #就是那个Type字段\n",
    "data['Type'].value_counts()#有4种  DEBIT表借账  TRANSFER表示转账  PAYMENT支付 CASH表示现金"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_weekday</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>91.250000</td>\n",
       "      <td>314.640015</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2018 22:56</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>HollowayCally</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>22.860001</td>\n",
       "      <td>304.809998</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 11:45</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>TateTana</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>95.180000</td>\n",
       "      <td>288.420013</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 10:42</td>\n",
       "      <td>First Class</td>\n",
       "      <td>TerrellConstance</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>24.580000</td>\n",
       "      <td>245.809998</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 8:15</td>\n",
       "      <td>First Class</td>\n",
       "      <td>McfaddenNatalie</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-259.579987</td>\n",
       "      <td>324.470001</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 7:33</td>\n",
       "      <td>First Class</td>\n",
       "      <td>LancasterSade</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180505</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>112.669998</td>\n",
       "      <td>359.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Highland</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 13:28</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>BriggsChristine</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180508</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>339.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Waipahu</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 7:10</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>FuentesRichard</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180511</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>157.429993</td>\n",
       "      <td>327.980011</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Chula Vista</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 6:28</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SmithOlivia</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180512</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>86.400002</td>\n",
       "      <td>319.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/22/2016 6:07</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MaddenMary</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>ClarkRonald</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>69295 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "0       DEBIT                         3                              4   \n",
       "3       DEBIT                         3                              4   \n",
       "6       DEBIT                         2                              1   \n",
       "13      DEBIT                         2                              1   \n",
       "15      DEBIT                         2                              1   \n",
       "...       ...                       ...                            ...   \n",
       "180505  DEBIT                         2                              4   \n",
       "180508  DEBIT                         4                              2   \n",
       "180511  DEBIT                         2                              2   \n",
       "180512  DEBIT                         6                              4   \n",
       "180515  DEBIT                         3                              2   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "0               91.250000          314.640015  Advance shipping   \n",
       "3               22.860001          304.809998  Advance shipping   \n",
       "6               95.180000          288.420013     Late delivery   \n",
       "13              24.580000          245.809998     Late delivery   \n",
       "15            -259.579987          324.470001     Late delivery   \n",
       "...                   ...                 ...               ...   \n",
       "180505         112.669998          359.980011  Advance shipping   \n",
       "180508          85.000000          339.980011     Late delivery   \n",
       "180511         157.429993          327.980011  Shipping on time   \n",
       "180512          86.400002          319.980011     Late delivery   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "0                        0           73  Sporting Goods        Caguas  ...   \n",
       "3                        0           73  Sporting Goods   Los Angeles  ...   \n",
       "6                        1           73  Sporting Goods        Caguas  ...   \n",
       "13                       1           73  Sporting Goods        Caguas  ...   \n",
       "15                       1           73  Sporting Goods        Caguas  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180505                   0           45         Fishing      Highland  ...   \n",
       "180508                   1           45         Fishing       Waipahu  ...   \n",
       "180511                   0           45         Fishing   Chula Vista  ...   \n",
       "180512                   1           45         Fishing        Caguas  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "\n",
       "                                     Product Name Product Price  \\\n",
       "0                                    Smart watch     327.750000   \n",
       "3                                    Smart watch     327.750000   \n",
       "6                                    Smart watch     327.750000   \n",
       "13                                   Smart watch     327.750000   \n",
       "15                                   Smart watch     327.750000   \n",
       "...                                           ...           ...   \n",
       "180505  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180508  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180511  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180512  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status  shipping date (DateOrders)   Shipping Mode  \\\n",
       "0                   0              2/3/2018 22:56  Standard Class   \n",
       "3                   0             1/16/2018 11:45  Standard Class   \n",
       "6                   0             1/15/2018 10:42     First Class   \n",
       "13                  0              1/15/2018 8:15     First Class   \n",
       "15                  0              1/15/2018 7:33     First Class   \n",
       "...               ...                         ...             ...   \n",
       "180505              0             1/18/2016 13:28  Standard Class   \n",
       "180508              0              1/20/2016 7:10    Second Class   \n",
       "180511              0              1/18/2016 6:28    Second Class   \n",
       "180512              0              1/22/2016 6:07  Standard Class   \n",
       "180515              0              1/19/2016 1:34    Second Class   \n",
       "\n",
       "       Customer Full Name order_year order_month order_weekday  order_hour  \n",
       "0           HollowayCally       2018           1             2          22  \n",
       "3                TateTana       2018           1             5          11  \n",
       "6        TerrellConstance       2018           1             5          10  \n",
       "13        McfaddenNatalie       2018           1             5           8  \n",
       "15          LancasterSade       2018           1             5           7  \n",
       "...                   ...        ...         ...           ...         ...  \n",
       "180505    BriggsChristine       2016           1             5          13  \n",
       "180508     FuentesRichard       2016           1             5           7  \n",
       "180511        SmithOlivia       2016           1             5           6  \n",
       "180512         MaddenMary       2016           1             5           6  \n",
       "180515        ClarkRonald       2016           1             5           1  \n",
       "\n",
       "[69295 rows x 58 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pay_type1=data[data['Type']=='DEBIT']\n",
    "pay_type2=data[data['Type']=='TRANSFER']\n",
    "pay_type3=data[data['Type']=='PAYMENT']\n",
    "pay_type4=data[data['Type']=='CASH']\n",
    "pay_type1#4中支付类型已经拿到了  下面看看不同 region的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Central America    10804\n",
       "Western Europe     10362\n",
       "South America       5536\n",
       "Oceania             3985\n",
       "Northern Europe     3807\n",
       "Southern Europe     3656\n",
       "Southeast Asia      3567\n",
       "West of USA         3187\n",
       "Caribbean           3101\n",
       "South Asia          3009\n",
       "East of USA         2776\n",
       "Eastern Asia        2634\n",
       "US Center           2317\n",
       "West Asia           2306\n",
       "Eastern Europe      1577\n",
       "South of  USA       1525\n",
       "West Africa         1502\n",
       "North Africa        1235\n",
       "East Africa          737\n",
       "Central Africa       659\n",
       "Southern Africa      485\n",
       "Canada               311\n",
       "Central Asia         217\n",
       "Name: Order Region, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#获取4种支付方式中  不同地区的特点\n",
    "count1=pay_type1['Order Region'].value_counts()\n",
    "count1#第一种支付方式DEBIT中 地区扽不情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_num=len(count1)\n",
    "region_num#这个数目是地区的数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "count2=pay_type2['Order Region'].value_counts()\n",
    "count3=pay_type3['Order Region'].value_counts()\n",
    "count4=pay_type4['Order Region'].value_counts()#同样的可以拿到另外三种的解决方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Central America', 'Western Europe', 'South America', 'Oceania',\n",
      "       'Northern Europe', 'Southern Europe', 'Southeast Asia', 'West of USA ',\n",
      "       'Caribbean', 'South Asia', 'East of USA', 'Eastern Asia', 'US Center ',\n",
      "       'West Asia', 'Eastern Europe', 'South of  USA ', 'West Africa',\n",
      "       'North Africa', 'East Africa', 'Central Africa', 'Southern Africa',\n",
      "       'Canada', 'Central Asia'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#下面开始画图了\n",
    "import numpy as np\n",
    "fig,ax=plt.subplots(figsize=(20,8))\n",
    "#设置一下索引的范围\n",
    "index=np.arange(region_num)#这个就是地区的数量\n",
    "#设置一下每种类型的显示间隔\n",
    "bar_width=0.2\n",
    "type1=plt.bar(index,count1,bar_width,color='b',label='DEBIT')\n",
    "type2=plt.bar(index+bar_width,count2,bar_width,color='r',label='TRANSFER')\n",
    "type3=plt.bar(index+bar_width*2,count3,bar_width,color='g',label='PAYMENT')\n",
    "type4=plt.bar(index+bar_width*3,count4,bar_width,color='y',label='CASH')\n",
    "plt.xlabel(\"地区\")\n",
    "plt.ylabel(\"支付数量\")\n",
    "plt.title(\"不同地区的支付数量分布\")\n",
    "plt.legend()#显示图例\n",
    "#显示刻度\n",
    "names=pay_type1['Order Region'].value_counts().keys()\n",
    "print(names)\n",
    "plt.xticks(index+bar_width,names,rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析结论\n",
    "DEBIT借记是所有地区中使用的最多的支付方式\n",
    "CASH现金 是所有地区中使用最少的支付方式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180519, 58)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.7对负收益的订单进行数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_weekday</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>-249.089996</td>\n",
       "      <td>311.359985</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2018 12:27</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LunaIrene</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CASH</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-247.779999</td>\n",
       "      <td>309.720001</td>\n",
       "      <td>Shipping on time</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/17/2018 12:06</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>MaldonadoGillian</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-259.579987</td>\n",
       "      <td>324.470001</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 7:33</td>\n",
       "      <td>First Class</td>\n",
       "      <td>LancasterSade</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>-246.360001</td>\n",
       "      <td>321.200012</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Canovanas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2018 7:12</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>GilesBrynne</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-17.139999</td>\n",
       "      <td>272.029999</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/16/2018 3:00</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>KellyEvelyn</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180495</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-239.990006</td>\n",
       "      <td>299.989990</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Roswell</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 22:35</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>StewartJohn</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180498</th>\n",
       "      <td>CASH</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>-337.100006</td>\n",
       "      <td>391.980011</td>\n",
       "      <td>Advance shipping</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Lindenhurst</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 19:05</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GouldMary</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180499</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>-258.779999</td>\n",
       "      <td>387.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/20/2016 17:41</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MasseyAngela</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180506</th>\n",
       "      <td>PAYMENT</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-65.470001</td>\n",
       "      <td>351.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/18/2016 11:01</td>\n",
       "      <td>First Class</td>\n",
       "      <td>SmithLarry</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180515</th>\n",
       "      <td>DEBIT</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>-613.770019</td>\n",
       "      <td>395.980011</td>\n",
       "      <td>Late delivery</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2016 1:34</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>ClarkRonald</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33784 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "1       TRANSFER                         5                              4   \n",
       "2           CASH                         4                              4   \n",
       "15         DEBIT                         2                              1   \n",
       "16       PAYMENT                         5                              2   \n",
       "28         DEBIT                         3                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180495   PAYMENT                         3                              4   \n",
       "180498      CASH                         3                              4   \n",
       "180499     DEBIT                         4                              2   \n",
       "180506   PAYMENT                         2                              1   \n",
       "180515     DEBIT                         3                              2   \n",
       "\n",
       "        Benefit per order  Sales per customer   Delivery Status  \\\n",
       "1             -249.089996          311.359985     Late delivery   \n",
       "2             -247.779999          309.720001  Shipping on time   \n",
       "15            -259.579987          324.470001     Late delivery   \n",
       "16            -246.360001          321.200012     Late delivery   \n",
       "28             -17.139999          272.029999  Advance shipping   \n",
       "...                   ...                 ...               ...   \n",
       "180495        -239.990006          299.989990  Advance shipping   \n",
       "180498        -337.100006          391.980011  Advance shipping   \n",
       "180499        -258.779999          387.980011     Late delivery   \n",
       "180506         -65.470001          351.980011     Late delivery   \n",
       "180515        -613.770019          395.980011     Late delivery   \n",
       "\n",
       "        Late_delivery_risk  Category Id   Category Name Customer City  ...  \\\n",
       "1                        1           73  Sporting Goods        Caguas  ...   \n",
       "2                        0           73  Sporting Goods      San Jose  ...   \n",
       "15                       1           73  Sporting Goods        Caguas  ...   \n",
       "16                       1           73  Sporting Goods     Canovanas  ...   \n",
       "28                       0           73  Sporting Goods     Roseville  ...   \n",
       "...                    ...          ...             ...           ...  ...   \n",
       "180495                   0           45         Fishing       Roswell  ...   \n",
       "180498                   0           45         Fishing   Lindenhurst  ...   \n",
       "180499                   1           45         Fishing        Caguas  ...   \n",
       "180506                   1           45         Fishing   San Antonio  ...   \n",
       "180515                   1           45         Fishing   Bakersfield  ...   \n",
       "\n",
       "                                     Product Name Product Price  \\\n",
       "1                                    Smart watch     327.750000   \n",
       "2                                    Smart watch     327.750000   \n",
       "15                                   Smart watch     327.750000   \n",
       "16                                   Smart watch     327.750000   \n",
       "28                                   Smart watch     327.750000   \n",
       "...                                           ...           ...   \n",
       "180495  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180498  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180499  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180506  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180515  Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status  shipping date (DateOrders)   Shipping Mode  \\\n",
       "1                   0             1/18/2018 12:27  Standard Class   \n",
       "2                   0             1/17/2018 12:06  Standard Class   \n",
       "15                  0              1/15/2018 7:33     First Class   \n",
       "16                  0              1/18/2018 7:12    Second Class   \n",
       "28                  0              1/16/2018 3:00  Standard Class   \n",
       "...               ...                         ...             ...   \n",
       "180495              0             1/19/2016 22:35  Standard Class   \n",
       "180498              0             1/19/2016 19:05  Standard Class   \n",
       "180499              0             1/20/2016 17:41    Second Class   \n",
       "180506              0             1/18/2016 11:01     First Class   \n",
       "180515              0              1/19/2016 1:34    Second Class   \n",
       "\n",
       "       Customer Full Name order_year order_month order_weekday  order_hour  \n",
       "1               LunaIrene       2018           1             5          12  \n",
       "2        MaldonadoGillian       2018           1             5          12  \n",
       "15          LancasterSade       2018           1             5           7  \n",
       "16            GilesBrynne       2018           1             5           7  \n",
       "28            KellyEvelyn       2018           1             5           3  \n",
       "...                   ...        ...         ...           ...         ...  \n",
       "180495        StewartJohn       2016           1             5          22  \n",
       "180498          GouldMary       2016           1             5          19  \n",
       "180499       MasseyAngela       2016           1             5          17  \n",
       "180506         SmithLarry       2016           1             5          11  \n",
       "180515        ClarkRonald       2016           1             5           1  \n",
       "\n",
       "[33784 rows x 58 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对负收益的订单进行一下数据探索\n",
    "loss=data[data['Benefit per order']<0]\n",
    "loss#一共有18万笔订单  其中负收益的订单是3万多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleats                  4590\n",
      "Men's Footwear          4169\n",
      "Women's Apparel         3923\n",
      "Indoor/Outdoor Games    3617\n",
      "Fishing                 3209\n",
      "Water Sports            2924\n",
      "Camping & Hiking        2590\n",
      "Cardio Equipment        2332\n",
      "Shop By Sport           2154\n",
      "Electronics              562\n",
      "Name: Category Name, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'订单里面负收益的订单数量产品类别Top10'}>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#拿到负收益的订单了之后 再来看看   里面Category Name商品类别 的分布情况\n",
    "print(loss['Category Name'].value_counts().nlargest(10))#这里只打印前10个\n",
    "#可以通过这个数据结构直接做可视化\n",
    "loss['Category Name'].value_counts().nlargest(10).plot.bar(figsize=(20,8),title='订单里面负收益的订单数量产品类别Top10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'订单里面负收益的订单数量地区Top10'}>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#同时显示一下负收益地区的top10  跟刚才那个是一样的\n",
    "loss['Order Region'].value_counts().nlargest(10).plot.bar(figsize=(20,8),title='订单里面负收益的订单数量地区Top10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总损失 -3883547.345768667\n"
     ]
    }
   ],
   "source": [
    "#所有的负收益的产品带来的损失是多少呢\n",
    "print(\"总损失\",loss['Benefit per order'].sum())#这是这张图里面的所有损失   这些损失可能来自一些欺诈的方法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.8欺诈订单分析（支付方式、地区、哪些人欺诈订单较多）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "COMPLETE           59491\n",
       "PENDING_PAYMENT    39832\n",
       "PROCESSING         21902\n",
       "PENDING            20227\n",
       "CLOSED             19616\n",
       "ON_HOLD             9804\n",
       "SUSPECTED_FRAUD     4062\n",
       "CANCELED            3692\n",
       "PAYMENT_REVIEW      1893\n",
       "Name: Order Status, dtype: int64"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#看一下订单状态那里的数据分布\n",
    "data['Order Status'].value_counts()\n",
    "# complete表示完整订单 pending_payment表示待付款 processing表示正在付款  PENDING表示待定 CLOSED关闭  ON_HOLD表示接听\n",
    "# SUSPECTED_FRAUD涉嫌欺诈 CANCELED取消  PAYMENT_REVIEW 付款审查"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Days for shipping (real)</th>\n",
       "      <th>Days for shipment (scheduled)</th>\n",
       "      <th>Benefit per order</th>\n",
       "      <th>Sales per customer</th>\n",
       "      <th>Delivery Status</th>\n",
       "      <th>Late_delivery_risk</th>\n",
       "      <th>Category Id</th>\n",
       "      <th>Category Name</th>\n",
       "      <th>Customer City</th>\n",
       "      <th>...</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Product Price</th>\n",
       "      <th>Product Status</th>\n",
       "      <th>shipping date (DateOrders)</th>\n",
       "      <th>Shipping Mode</th>\n",
       "      <th>Customer Full Name</th>\n",
       "      <th>order_year</th>\n",
       "      <th>order_month</th>\n",
       "      <th>order_weekday</th>\n",
       "      <th>order_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>130.580002</td>\n",
       "      <td>272.029999</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/19/2018 9:18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>ShortGermane</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>90.279999</td>\n",
       "      <td>288.420013</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>73</td>\n",
       "      <td>Sporting Goods</td>\n",
       "      <td>Billings</td>\n",
       "      <td>...</td>\n",
       "      <td>Smart watch</td>\n",
       "      <td>327.750000</td>\n",
       "      <td>0</td>\n",
       "      <td>1/15/2018 4:24</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>VanceSimone</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>128.220001</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>Electronics</td>\n",
       "      <td>Freeport</td>\n",
       "      <td>...</td>\n",
       "      <td>Under Armour Men's Compression EV SL Slide</td>\n",
       "      <td>44.990002</td>\n",
       "      <td>0</td>\n",
       "      <td>5/13/2016 17:42</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PetersenPatricia</td>\n",
       "      <td>2016</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>133.910004</td>\n",
       "      <td>278.970001</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Cardio Equipment</td>\n",
       "      <td>Fort Washington</td>\n",
       "      <td>...</td>\n",
       "      <td>Nike Men's Free 5.0+ Running Shoe</td>\n",
       "      <td>99.989998</td>\n",
       "      <td>0</td>\n",
       "      <td>4/7/2016 19:51</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>PetersenJulie</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>79.160004</td>\n",
       "      <td>272.970001</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Cardio Equipment</td>\n",
       "      <td>Bakersfield</td>\n",
       "      <td>...</td>\n",
       "      <td>Nike Men's Free 5.0+ Running Shoe</td>\n",
       "      <td>99.989998</td>\n",
       "      <td>0</td>\n",
       "      <td>8/20/2016 2:51</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SmithLisa</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180274</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>163.190002</td>\n",
       "      <td>339.980011</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>2/3/2016 3:49</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SmithWilliam</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180309</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>114.830002</td>\n",
       "      <td>347.980011</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Modesto</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/28/2016 21:52</td>\n",
       "      <td>First Class</td>\n",
       "      <td>SmithJames</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180352</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/29/2016 3:08</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SmithMary</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180406</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>-333.179993</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Caguas</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/28/2016 12:46</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>McguireAnthony</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180490</th>\n",
       "      <td>TRANSFER</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>-59.840000</td>\n",
       "      <td>339.980011</td>\n",
       "      <td>Shipping canceled</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Fishing</td>\n",
       "      <td>Madera</td>\n",
       "      <td>...</td>\n",
       "      <td>Field &amp; Stream Sportsman 16 Gun Fire Safe</td>\n",
       "      <td>399.980011</td>\n",
       "      <td>0</td>\n",
       "      <td>1/21/2016 5:56</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>GuerraMary</td>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4062 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Type  Days for shipping (real)  Days for shipment (scheduled)  \\\n",
       "10      TRANSFER                         6                              2   \n",
       "24      TRANSFER                         2                              2   \n",
       "183     TRANSFER                         5                              4   \n",
       "184     TRANSFER                         5                              4   \n",
       "185     TRANSFER                         6                              4   \n",
       "...          ...                       ...                            ...   \n",
       "180274  TRANSFER                         5                              2   \n",
       "180309  TRANSFER                         2                              1   \n",
       "180352  TRANSFER                         4                              4   \n",
       "180406  TRANSFER                         6                              4   \n",
       "180490  TRANSFER                         4                              4   \n",
       "\n",
       "        Benefit per order  Sales per customer    Delivery Status  \\\n",
       "10             130.580002          272.029999  Shipping canceled   \n",
       "24              90.279999          288.420013  Shipping canceled   \n",
       "183             28.850000          128.220001  Shipping canceled   \n",
       "184            133.910004          278.970001  Shipping canceled   \n",
       "185             79.160004          272.970001  Shipping canceled   \n",
       "...                   ...                 ...                ...   \n",
       "180274         163.190002          339.980011  Shipping canceled   \n",
       "180309         114.830002          347.980011  Shipping canceled   \n",
       "180352          90.000000          399.980011  Shipping canceled   \n",
       "180406        -333.179993          399.980011  Shipping canceled   \n",
       "180490         -59.840000          339.980011  Shipping canceled   \n",
       "\n",
       "        Late_delivery_risk  Category Id     Category Name    Customer City  \\\n",
       "10                       0           73    Sporting Goods           Caguas   \n",
       "24                       0           73    Sporting Goods         Billings   \n",
       "183                      0           13       Electronics         Freeport   \n",
       "184                      0            9  Cardio Equipment  Fort Washington   \n",
       "185                      0            9  Cardio Equipment      Bakersfield   \n",
       "...                    ...          ...               ...              ...   \n",
       "180274                   0           45           Fishing           Caguas   \n",
       "180309                   0           45           Fishing          Modesto   \n",
       "180352                   0           45           Fishing      Albuquerque   \n",
       "180406                   0           45           Fishing           Caguas   \n",
       "180490                   0           45           Fishing           Madera   \n",
       "\n",
       "        ...                                Product Name Product Price  \\\n",
       "10      ...                                Smart watch     327.750000   \n",
       "24      ...                                Smart watch     327.750000   \n",
       "183     ...  Under Armour Men's Compression EV SL Slide     44.990002   \n",
       "184     ...           Nike Men's Free 5.0+ Running Shoe     99.989998   \n",
       "185     ...           Nike Men's Free 5.0+ Running Shoe     99.989998   \n",
       "...     ...                                         ...           ...   \n",
       "180274  ...   Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180309  ...   Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180352  ...   Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180406  ...   Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "180490  ...   Field & Stream Sportsman 16 Gun Fire Safe    399.980011   \n",
       "\n",
       "       Product Status  shipping date (DateOrders)   Shipping Mode  \\\n",
       "10                  0              1/19/2018 9:18    Second Class   \n",
       "24                  0              1/15/2018 4:24    Second Class   \n",
       "183                 0             5/13/2016 17:42  Standard Class   \n",
       "184                 0              4/7/2016 19:51  Standard Class   \n",
       "185                 0              8/20/2016 2:51  Standard Class   \n",
       "...               ...                         ...             ...   \n",
       "180274              0               2/3/2016 3:49    Second Class   \n",
       "180309              0             1/28/2016 21:52     First Class   \n",
       "180352              0              1/29/2016 3:08  Standard Class   \n",
       "180406              0             1/28/2016 12:46  Standard Class   \n",
       "180490              0              1/21/2016 5:56  Standard Class   \n",
       "\n",
       "       Customer Full Name order_year order_month order_weekday  order_hour  \n",
       "10           ShortGermane       2018           1             5           9  \n",
       "24            VanceSimone       2018           1             5           4  \n",
       "183      PetersenPatricia       2016           5             6          17  \n",
       "184         PetersenJulie       2016           4             5          19  \n",
       "185             SmithLisa       2016           8             6           2  \n",
       "...                   ...        ...         ...           ...         ...  \n",
       "180274       SmithWilliam       2016           1             4           3  \n",
       "180309         SmithJames       2016           1             1          21  \n",
       "180352          SmithMary       2016           1             0           3  \n",
       "180406     McguireAnthony       2016           1             4          12  \n",
       "180490         GuerraMary       2016           1             6           5  \n",
       "\n",
       "[4062 rows x 58 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['Order Status']=='SUSPECTED_FRAUD']#这里是所有的欺诈订单"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TRANSFER    4062\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#拿到所有的欺诈订单之后 看看欺诈类型中 交易方式是那种\n",
    "data[data['Order Status']=='SUSPECTED_FRAUD']['Type'].value_counts()#根据结果来看 是转账的方式 全是转账的方式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TRANSFER容易导致欺诈交易"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#看看欺诈订单都是来自哪些地区\n",
    "high_fraud=data[data['Order Status']=='SUSPECTED_FRAUD']\n",
    "high_fraud['Order Region'].value_counts().plot.bar(figsize=(20,8))\n",
    "plt.title('不同地区的欺诈订单数量')\n",
    "plt.ylabel('欺诈订单的数量')\n",
    "plt.xlabel('地区')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Wstern Europe地区欺诈交易最多，将一个bar分成2个部分(堆积条形图)\n",
    "#high_fraud_total就拿到订单里面所有欺诈类型的数据\n",
    "high_fraud_total=data[data['Order Status']=='SUSPECTED_FRAUD']\n",
    "#拿到订单类型是欺诈并且地区是欧洲的数据\n",
    "high_fraud_we=data[(data['Order Status']=='SUSPECTED_FRAUD')&(data['Order Region']=='Western Europe')]\n",
    "#找出 所有欺诈订单 里面风险最高的10个Category 商品类别\n",
    "fruad1=high_fraud_total['Category Name'].value_counts().nlargest(10).plot.bar(figsize=(20,8),title=' Fruad Category',color='orange')\n",
    "#找出 欧洲欺诈订单 里面风险最高的10个Category 商品类别\n",
    "fruad2=high_fraud_we['Category Name'].value_counts().nlargest(10).plot.bar(figsize=(20,8),title=' Fruad Category in Western Europe',color='green')\n",
    "plt.title('Top10 最容易欺诈的订单')\n",
    "plt.xlabel('产品类别')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Top10有风险订单的人'}>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#筛选出top10 Customer（风险）\n",
    "cus=data[data['Order Status']=='SUSPECTED_FRAUD']#这个cus就拿到了所有的欺诈订单\n",
    "#然后在欺诈订单中 把姓名排序\n",
    "cus['Customer Full Name'].value_counts().nlargest(10).plot.bar(figsize=(20,8),title='Top10有风险订单的人')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4771645.883297398\n",
      "102491.66191043999\n"
     ]
    }
   ],
   "source": [
    "#找到SmithMary的交易金额\n",
    "print(data[data['Customer Full Name']=='SmithMary']['Sales'].sum())#它一共买了多少钱\n",
    "print(data[(data['Customer Full Name']=='SmithMary')&(data['Order Status']=='SUSPECTED_FRAUD')]['Sales'].sum()) #是史密斯且订单还是欺诈的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4文件转存为pkl格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#这个时候data我们想保存一下 可以通过 pickle文件来保存  比其他文件更加方便 \n",
    "import pickle\n",
    "with open('mydata.pkl','wb') as file:\n",
    "    pickle.dump(data,file)"
   ]
  }
 ],
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